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Takahashi S, Sakaguchi Y, Kouno N, Takasawa K, Ishizu K, Akagi Y, Aoyama R, Teraya N, Bolatkan A, Shinkai N, Machino H, Kobayashi K, Asada K, Komatsu M, Kaneko S, Sugiyama M, Hamamoto R. Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. J Med Syst 2024; 48:84. [PMID: 39264388 PMCID: PMC11393140 DOI: 10.1007/s10916-024-02105-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 08/31/2024] [Indexed: 09/13/2024]
Abstract
In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.
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Affiliation(s)
- Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Yusuke Sakaguchi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Nobuji Kouno
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
- Department of Surgery, Graduate School of Medicine, Kyoto University, Yoshida-konoe-cho, Sakyo-ku, Kyoto, 606-8303, Japan
| | - Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Kenichi Ishizu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yu Akagi
- Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Rina Aoyama
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Naoki Teraya
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Amina Bolatkan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Masashi Sugiyama
- RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
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Lv F, Li X, Wang Z, Wang X, Liu J. Identification and validation of Rab GTPases RAB13 as biomarkers for peritoneal metastasis and immune cell infiltration in colorectal cancer patients. Front Immunol 2024; 15:1403008. [PMID: 39192986 PMCID: PMC11347351 DOI: 10.3389/fimmu.2024.1403008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 07/29/2024] [Indexed: 08/29/2024] Open
Abstract
Background As one of the most common cancer, colorectal cancer (CRC) is with high morbidity and mortality. Peritoneal metastasis (PM) is a fatal state of CRC, and few patients may benefit from traditional therapies. There is a complex interaction between PM and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with colorectal cancer peritoneal metastasis (CRCPM) and their relationship with immune cell infiltration. Methods By informatic analysis, differently expressed genes (DEGs) were selected and hub genes were screened out. RAB13, one of the hub genes, was identificated from public databases and validated in CRC tissues. The ESTIMATE, CEBERSORT and TIMER algorithms were applied to analyze the correlation between RAB13 and immune infiltration in CRC. RAB13's expression in different cells were analyzed at the single-cell level in scRNA-Seq. The Gene Set Enrichment Analysis (GSEA) was performed for RAB13 enrichment and further confirmed. Using oncoPredict algorithm, RAB13's impact on drug sensitivity was evaluated. Results High RAB13 expression was identified in public databases and led to a poor prognosis. RAB13 was found to be positively correlated with the macrophages and other immune cells infiltration and from scRNA-Seq, RAB13 was found to be located in CRC cells and macrophages. GSEA revealed that high RAB13 expression enriched in a various of biological signaling, and oncoPredict algorithm showed that RAB13 expression was correlated with paclitaxel sensitivity. Conclusion Our study indicated clinical role of RAB13 in CRC-PM, suggesting its potential as a therapeutic target in the future.
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Affiliation(s)
- Fei Lv
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaoqi Li
- Oncology Department III, People’s Hospital of Liaoning Province, Shenyang, Liaoning, China
| | - Zhe Wang
- Department of Digestive Diseases 1, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China
| | - Xiaobo Wang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jing Liu
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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Singh M, Kumar A, Khanna NN, Laird JR, Nicolaides A, Faa G, Johri AM, Mantella LE, Fernandes JFE, Teji JS, Singh N, Fouda MM, Singh R, Sharma A, Kitas G, Rathore V, Singh IM, Tadepalli K, Al-Maini M, Isenovic ER, Chaturvedi S, Garg D, Paraskevas KI, Mikhailidis DP, Viswanathan V, Kalra MK, Ruzsa Z, Saba L, Laine AF, Bhatt DL, Suri JS. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review. EClinicalMedicine 2024; 73:102660. [PMID: 38846068 PMCID: PMC11154124 DOI: 10.1016/j.eclinm.2024.102660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Background The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding No funding received.
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Affiliation(s)
- Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Bennett University, 201310, Greater Noida, India
| | - Ashish Kumar
- Bennett University, 201310, Greater Noida, India
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Gavino Faa
- Department of Pathology, University of Cagliari, Cagliari, Italy
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura E. Mantella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | | | - Jagjit S. Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, 22901, VA, USA
| | - George Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, DY1, Dudley, UK
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | | | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Esma R. Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 110010, Serbia
| | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | | | | | - Dimitri P. Mikhailidis
- Department of Clinical Biochemistry, Royal Free Hospital Campus, University College London Medical School, University College London (UCL), London, UK
| | | | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Andrew F. Laine
- Departments of Biomedical and Radiology, Columbia University, New York, NY, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
- Department of Computer Science, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
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Kuraz Abebe B, Wang J, Guo J, Wang H, Li A, Zan L. A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle. Gene 2024; 908:148295. [PMID: 38387707 DOI: 10.1016/j.gene.2024.148295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/23/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.
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Affiliation(s)
- Belete Kuraz Abebe
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; Department of Animal Science, Werabe University, P.O. Box 46, Werabe, Ethiopia
| | - Jianfang Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Juntao Guo
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Hongbao Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Anning Li
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China
| | - Linsen Zan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China; National Beef Cattle Improvement Center, Northwest A&F University, Yangling, Shaanxi 712100, People's Republic of China.
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N. Kachouie N, Deebani W, Shutaywi M, Christiani DC. Lung cancer clustering by identification of similarities and discrepancies of DNA copy numbers using maximal information coefficient. PLoS One 2024; 19:e0301131. [PMID: 38739669 PMCID: PMC11090345 DOI: 10.1371/journal.pone.0301131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/11/2024] [Indexed: 05/16/2024] Open
Abstract
Lung cancer is the second most diagnosed cancer and the first cause of cancer related death for men and women in the United States. Early detection is essential as patient survival is not optimal and recurrence rate is high. Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with the cancer. In this research, the similarities between cancer and paired peripheral blood samples are identified using maximal information coefficient (MIC) and the spatial locations with substantially high MIC scores in each chromosome are used for clustering analysis. The results showed that a sizable reduction of feature set can be obtained using only a subset of locations with high MIC values. The clustering performance was evaluated using both true rate and normalized mutual information (NMI). Clustering results using the reduced feature set outperformed the performance of clustering using entire feature set in several chromosomes that are highly associated with lung cancer with several identified oncogenes.
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Affiliation(s)
- Nezamoddin N. Kachouie
- Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL, United States of America
| | - Wejdan Deebani
- Mathematics Department, College of Science and Arts, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Meshal Shutaywi
- Mathematics Department, College of Science and Arts, King Abdulaziz University, Jeddah, Saudi Arabia
| | - David C. Christiani
- Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States of America
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Ju CC, Liu XX, Liu LH, Guo N, Guan LW, Wu JX, Liu DW. Epigenetic modification: A novel insight into diabetic wound healing. Heliyon 2024; 10:e28086. [PMID: 38533007 PMCID: PMC10963386 DOI: 10.1016/j.heliyon.2024.e28086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
Wound healing is an intricate and fine regulatory process. In diabetic patients, advanced glycation end products (AGEs), excessive reactive oxygen species (ROS), biofilm formation, persistent inflammation, and angiogenesis regression contribute to delayed wound healing. Epigenetics, the fast-moving science in the 21st century, has been up to date and associated with diabetic wound repair. In this review, we go over the functions of epigenetics in diabetic wound repair in retrospect, covering transcriptional and posttranscriptional regulation. Among these, we found that histone modification is widely involved in inflammation and angiogenesis by affecting macrophages and endothelial cells. DNA methylation is involved in factors regulation in wound repair but also affects the differentiation phenotype of cells in hyperglycemia. In addition, noncodingRNA regulation and RNA modification in diabetic wound repair were also generalized. The future prospects for epigenetic applications are discussed in the end. In conclusion, the study suggests that epigenetics is an integral regulatory mechanism in diabetic wound healing.
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Affiliation(s)
- Cong-Cong Ju
- Medical Center of Burn Plastic and Wound Repair, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, PR China
- Huankui Academy, Nanchang University, Nanchang, Jiangxi, PR China
| | - Xiao-Xiao Liu
- Medical Center of Burn Plastic and Wound Repair, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, PR China
| | - Li-hua Liu
- Huankui Academy, Nanchang University, Nanchang, Jiangxi, PR China
| | - Nan Guo
- Nanchang University, Nanchang, Jiangxi, PR China
| | - Le-wei Guan
- Huankui Academy, Nanchang University, Nanchang, Jiangxi, PR China
| | - Jun-xian Wu
- Nanchang University, Nanchang, Jiangxi, PR China
| | - De-Wu Liu
- Medical Center of Burn Plastic and Wound Repair, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, PR China
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Lu S, Yang J, Gu Y, He D, Wu H, Sun W, Xu D, Li C, Guo C. Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors. ACS Sens 2024; 9:1134-1148. [PMID: 38363978 DOI: 10.1021/acssensors.3c02670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.
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Affiliation(s)
- Shasha Lu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Jianyu Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Yu Gu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Dongyuan He
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Haocheng Wu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Wei Sun
- College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Changming Li
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Chunxian Guo
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
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Takasawa K, Asada K, Kaneko S, Shiraishi K, Machino H, Takahashi S, Shinkai N, Kouno N, Kobayashi K, Komatsu M, Mizuno T, Okubo Y, Mukai M, Yoshida T, Yoshida Y, Horinouchi H, Watanabe SI, Ohe Y, Yatabe Y, Kohno T, Hamamoto R. Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability. Exp Mol Med 2024; 56:646-655. [PMID: 38433247 PMCID: PMC10985003 DOI: 10.1038/s12276-024-01173-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/27/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.
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Affiliation(s)
- Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Nobuji Kouno
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
| | - Takaaki Mizuno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
- Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yu Okubo
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Masami Mukai
- Division of Medical Informatics, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Tatsuya Yoshida
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Hidehito Horinouchi
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yuichiro Ohe
- Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, 104-0045, Japan
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan.
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9
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Dezem FS, Marção M, Ben-Cheikh B, Nikulina N, Omotoso A, Burnett D, Coelho P, Hurley J, Gomez C, Phan-Everson T, Ong G, Martelotto L, Lewis ZR, George S, Braubach O, Malta TM, Plummer J. A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics. BMC Genomics 2023; 24:717. [PMID: 38017371 PMCID: PMC10683105 DOI: 10.1186/s12864-023-09722-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/07/2023] [Indexed: 11/30/2023] Open
Abstract
Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.
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Affiliation(s)
- Felipe Segato Dezem
- Center for Spatial Omics, St Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, TN, USA
- Department of Clinical Analysis, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Maycon Marção
- Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, TN, USA
- Department of Clinical Analysis, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Bassem Ben-Cheikh
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - Nadya Nikulina
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - Ayodele Omotoso
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, UHealth Medical Systems, Miami, FL, USA
| | - Destiny Burnett
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, UHealth Medical Systems, Miami, FL, USA
| | - Priscila Coelho
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, UHealth Medical Systems, Miami, FL, USA
| | - Judith Hurley
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, UHealth Medical Systems, Miami, FL, USA
| | - Carmen Gomez
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, UHealth Medical Systems, Miami, FL, USA
| | | | - Giang Ong
- Nanostring Technologies, Seattle, WA, USA
| | | | | | - Sophia George
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, UHealth Medical Systems, Miami, FL, USA
| | - Oliver Braubach
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - Tathiane M Malta
- Department of Clinical Analysis, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Jasmine Plummer
- Center for Spatial Omics, St Jude Children's Research Hospital, Memphis, TN, USA.
- Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, TN, USA.
- Department of Cellular & Molecular Biology, St Jude Children's Research Hospital, Memphis, TN, USA.
- Comprehensive Cancer Center, St Jude Children's Research Hospital, Memphis, TN, USA.
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10
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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11
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Suárez M, Martínez R, Torres AM, Ramón A, Blasco P, Mateo J. Personalized Risk Assessment of Hepatic Fibrosis after Cholecystectomy in Metabolic-Associated Steatotic Liver Disease: A Machine Learning Approach. J Clin Med 2023; 12:6489. [PMID: 37892625 PMCID: PMC10607671 DOI: 10.3390/jcm12206489] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 10/29/2023] Open
Abstract
Metabolic Associated Fatty Liver Disease (MASLD) is a condition that is often present in patients with a history of cholecystectomy. This is because both situations share interconnected metabolic pathways. This study aimed to establish a predictive model that allows for the identification of patients at risk of developing hepatic fibrosis following this surgery, with potential implications for surgical decision-making. A retrospective cross-sectional analysis was conducted in four hospitals using a database of 211 patients with MASLD who underwent cholecystectomy. MASLD diagnosis was established through liver biopsy or FibroScan, and non-invasive test scores were included for analysis. Various Machine Learning (ML) methods were employed, with the Adaptive Boosting (Adaboost) system selected to build the predictive model. Platelet level emerged as the most crucial variable in the predictive model, followed by dyslipidemia and type-2 diabetes mellitus. FIB-4 score proved to be the most reliable non-invasive test. The Adaboost algorithm improved the results compared to the other methods, excelling in both accuracy and area under the curve (AUC). Moreover, this system holds promise for implementation in hospitals as a valuable diagnostic support tool. In conclusion, platelet level (<150,000/dL), dyslipidemia, and type-2 diabetes mellitus were identified as primary risk factors for liver fibrosis in MASLD patients following cholecystectomy. FIB-4 score is recommended for decision-making, particularly when the indication for surgery is uncertain. This predictive model offers valuable insights into risk stratification and personalized patient management in post-cholecystectomy MASLD cases.
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Affiliation(s)
- Miguel Suárez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain; (M.S.)
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 160071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Raquel Martínez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain; (M.S.)
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 160071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 160071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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12
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Hicks EM, Seah C, Cote A, Marchese S, Brennand KJ, Nestler EJ, Girgenti MJ, Huckins LM. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Transl Psychiatry 2023; 13:129. [PMID: 37076454 PMCID: PMC10115809 DOI: 10.1038/s41398-023-02412-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Alanna Cote
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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13
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Ben David G, Amir Y, Salalha R, Sharvit L, Richter-Levin G, Atzmon G. Can Epigenetics Predict Drug Efficiency in Mental Disorders? Cells 2023; 12:1173. [PMID: 37190082 PMCID: PMC10136455 DOI: 10.3390/cells12081173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/23/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Psychiatric disorders affect millions of individuals and their families worldwide, and the costs to society are substantial and are expected to rise due to a lack of effective treatments. Personalized medicine-customized treatment tailored to the individual-offers a solution. Although most mental diseases are influenced by genetic and environmental factors, finding genetic biomarkers that predict treatment efficacy has been challenging. This review highlights the potential of epigenetics as a tool for predicting treatment efficacy and personalizing medicine for psychiatric disorders. We examine previous studies that have attempted to predict treatment efficacy through epigenetics, provide an experimental model, and note the potential challenges at each stage. While the field is still in its infancy, epigenetics holds promise as a predictive tool by examining individual patients' epigenetic profiles in conjunction with other indicators. However, further research is needed, including additional studies, replication, validation, and application beyond clinical settings.
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Affiliation(s)
- Gil Ben David
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (G.B.D.); (R.S.)
| | - Yam Amir
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (Y.A.)
| | - Randa Salalha
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (G.B.D.); (R.S.)
| | - Lital Sharvit
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (Y.A.)
| | - Gal Richter-Levin
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (G.B.D.); (R.S.)
- Department of Psychology, Faculty of Social Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel
- Integrated Brain and Behavior Research Center (IBBR), University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel
| | - Gil Atzmon
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel; (Y.A.)
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14
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Yang M, Matan-Lithwick S, Wang Y, De Jager PL, Bennett DA, Felsky D. Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing. Brain Commun 2023; 5:fcad110. [PMID: 37082508 PMCID: PMC10110975 DOI: 10.1093/braincomms/fcad110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/17/2023] [Accepted: 04/03/2023] [Indexed: 04/07/2023] Open
Abstract
Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (p Bonf = 5.9 × 10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.
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Affiliation(s)
- Mu Yang
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Stuart Matan-Lithwick
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Yanling Wang
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Philip L De Jager
- The Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY 10033, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Daniel Felsky
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
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15
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Marino N, Putignano G, Cappilli S, Chersoni E, Santuccione A, Calabrese G, Bischof E, Vanhaelen Q, Zhavoronkov A, Scarano B, Mazzotta AD, Santus E. Towards AI-driven longevity research: An overview. FRONTIERS IN AGING 2023; 4:1057204. [PMID: 36936271 PMCID: PMC10018490 DOI: 10.3389/fragi.2023.1057204] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 02/06/2023] [Indexed: 03/06/2023]
Abstract
While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.
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Affiliation(s)
- Nicola Marino
- Women’s Brain Project (WBP), Gunterhausen, Switzerland
| | | | - Simone Cappilli
- Dermatology, Catholic University of the Sacred Heart, Rome, Italy
- UOC of Dermatology, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, A. Gemelli University Hospital Foundation-IRCCS, Rome, Italy
| | - Emmanuele Chersoni
- Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China
| | | | - Giuliana Calabrese
- Department of Translational Medicine and Surgery, CatholicUniversity of the Sacred Heart, Rome, Italy
| | - Evelyne Bischof
- Insilico Medicine Hong Kong Ltd., New Territories, Hong Kong SAR, China
| | - Quentin Vanhaelen
- Insilico Medicine Hong Kong Ltd., New Territories, Hong Kong SAR, China
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong Ltd., New Territories, Hong Kong SAR, China
| | - Bryan Scarano
- Department of Translational Medicine and Surgery, CatholicUniversity of the Sacred Heart, Rome, Italy
| | - Alessandro D. Mazzotta
- Department of Digestive, Oncological and Metabolic Surgery, Institute Mutualiste Montsouris, Paris, France
- Biorobotics Institute, Scuola Superiore Sant’anna, Pisa, Italy
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16
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Flores JE, Claborne DM, Weller ZD, Webb-Robertson BJM, Waters KM, Bramer LM. Missing data in multi-omics integration: Recent advances through artificial intelligence. Front Artif Intell 2023; 6:1098308. [PMID: 36844425 PMCID: PMC9949722 DOI: 10.3389/frai.2023.1098308] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
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Affiliation(s)
- Javier E. Flores
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Daniel M. Claborne
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Zachary D. Weller
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Katrina M. Waters
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Lisa M. Bramer
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
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Shozu K, Kaneko S, Shinkai N, Dozen A, Kosuge H, Nakakido M, Machino H, Takasawa K, Asada K, Komatsu M, Tsumoto K, Ohnuma SI, Hamamoto R. Repression of the PRELP gene is relieved by histone deacetylase inhibitors through acetylation of histone H2B lysine 5 in bladder cancer. Clin Epigenetics 2022; 14:147. [PMID: 36371227 PMCID: PMC9656081 DOI: 10.1186/s13148-022-01370-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Proline/arginine-rich end leucine-rich repeat protein (PRELP) is a member of the small leucine-rich proteoglycan family of extracellular matrix proteins, which is markedly suppressed in the majority of early-stage epithelial cancers and plays a role in regulating the epithelial-mesenchymal transition by altering cell-cell adhesion. Although PRELP is an important factor in the development and progression of bladder cancer, the mechanism of PRELP gene repression remains unclear. RESULTS Here, we show that repression of PRELP mRNA expression in bladder cancer cells is alleviated by HDAC inhibitors (HDACi) through histone acetylation. Using ChIP-qPCR analysis, we found that acetylation of lysine residue 5 of histone H2B in the PRELP gene promoter region is a marker for the de-repression of PRELP expression. CONCLUSIONS These results suggest a mechanism through which HDACi may partially regulate the function of PRELP to suppress the development and progression of bladder cancer. Some HDACi are already in clinical use, and the findings of this study provide a mechanistic basis for further investigation of HDACi-based therapeutic strategies.
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Affiliation(s)
- Kanto Shozu
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.267346.20000 0001 2171 836XDepartment of Obstetrics and Gynecology, University of Toyama, Toyama, Japan
| | - Syuzo Kaneko
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Norio Shinkai
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan ,grid.265073.50000 0001 1014 9130Department of NCC Cancer Science, Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ai Dozen
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan
| | - Hirofumi Kosuge
- grid.26999.3d0000 0001 2151 536XSchool of Engineering, The University of Tokyo, Tokyo, Japan
| | - Makoto Nakakido
- grid.26999.3d0000 0001 2151 536XSchool of Engineering, The University of Tokyo, Tokyo, Japan
| | - Hidenori Machino
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Ken Takasawa
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Ken Asada
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Masaaki Komatsu
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
| | - Kouhei Tsumoto
- grid.26999.3d0000 0001 2151 536XSchool of Engineering, The University of Tokyo, Tokyo, Japan
| | - Shin-Ichi Ohnuma
- grid.83440.3b0000000121901201UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London, EC1V 9EL UK ,grid.5335.00000000121885934Department of Oncology, The Hutchison/MRC Research Center, University of Cambridge, Hills Road, Cambridge, CB2 2XZ UK
| | - Ryuji Hamamoto
- grid.272242.30000 0001 2168 5385Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045 Japan ,grid.509456.bRIKEN Center for Advanced Intelligence Project, Cancer Translational Research Team, Tokyo, Japan
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Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, Chen T, Chen M, Ding Z, Niu G. Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges. Front Oncol 2022; 12:892056. [PMID: 35965542 PMCID: PMC9363668 DOI: 10.3389/fonc.2022.892056] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022] Open
Abstract
Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.
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Affiliation(s)
- Jiaona Xu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuting Meng
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kefan Qiu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Win Topatana
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wei
- Department of Neurology, Affiliated Ningbo First Hospital, Ningbo, China
| | - Tianwen Chen
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Guozhong Niu
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
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Dar MA, Arafah A, Bhat KA, Khan A, Khan MS, Ali A, Ahmad SM, Rashid SM, Rehman MU. Multiomics technologies: role in disease biomarker discoveries and therapeutics. Brief Funct Genomics 2022; 22:76-96. [PMID: 35809340 DOI: 10.1093/bfgp/elac017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/21/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes. SUMMARY POINTS
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Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, Asada K, Komatsu M, Okamoto K, Kameoka H, Kaneko S. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Brief Bioinform 2022; 23:6628783. [PMID: 35788277 PMCID: PMC9294421 DOI: 10.1093/bib/bbac246] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/06/2022] [Accepted: 05/25/2022] [Indexed: 12/19/2022] Open
Abstract
The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Rina Aoyama
- Showa University Graduate School of Medicine School of Medicine
| | | | - Ken Asada
- RIKEN Center for Advanced Intelligence Project
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21
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RETRACTED ARTICLE: Analysis on the preventive effect preventive initiatives for older adults using artificial intelligence techniques. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-020-00855-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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22
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes? ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:45-83. [PMID: 35871896 DOI: 10.1016/bs.apcsb.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.
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Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases. Genes (Basel) 2022; 13:genes13060935. [PMID: 35741697 PMCID: PMC9222484 DOI: 10.3390/genes13060935] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 12/12/2022] Open
Abstract
Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.
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Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning. Biomedicines 2022; 10:biomedicines10051082. [PMID: 35625819 PMCID: PMC9138644 DOI: 10.3390/biomedicines10051082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 02/05/2023] Open
Abstract
Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To address these clinical issues, more accurate and normalized automatic endocardial border detection would be valuable. Here, we develop a deep learning-based method for automated endocardial border detection and left ventricular functional assessment in two-dimensional echocardiographic videos. First, segmentation of the left ventricular cavity was performed in the six representative projections for a cardiac cycle. We employed four segmentation methods: U-Net, UNet++, UNet3+, and Deep Residual U-Net. UNet++ and UNet3+ showed a sufficiently high performance in the mean value of intersection over union and Dice coefficient. The accuracy of the four segmentation methods was then evaluated by calculating the mean value for the estimation error of the echocardiographic indexes. UNet++ was superior to the other segmentation methods, with the acceptable mean estimation error of the left ventricular ejection fraction of 10.8%, global longitudinal strain of 8.5%, and global circumferential strain of 5.8%, respectively. Our method using UNet++ demonstrated the best performance. This method may potentially support examiners and improve the workflow in echocardiography.
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Holubekova V, Kolkova Z, Kasubova I, Samec M, Mazurakova A, Koklesova L, Kubatka P, Rokos T, Kozubik E, Biringer K, Kudela E. Interaction of cervical microbiome with epigenome of epithelial cells: Significance of inflammation to primary healthcare. Biomol Concepts 2022; 13:61-80. [PMID: 35245973 DOI: 10.1515/bmc-2022-0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/08/2022] [Indexed: 12/12/2022] Open
Abstract
One pillar of the predictive, preventive, and personalized medicine framework strategies is the female health. The evaluation of women's lifestyle and dietary habits in context with genetic and modifiable risk factors may reflect the prevention of cervical cancer before the occurrence of clinical symptoms and prediction of cervical lesion behavior. The main aim of this review is to analyze publications in the field of precision medicine that allow the use of research knowledge of cervical microbiome, epigenetic modifications, and inflammation in potential application in clinical practice. Personalized approach in evaluating patient's risk of future development of cervical abnormality should consider the biomarkers of the local microenvironment characterized by the microbial composition, epigenetic pattern of cervical epithelium, and presence of chronic inflammation. Novel sequencing techniques enable a more detailed characterization of actual state in cervical epithelium. Better understanding of all changes in multiomics level enables a better assessment of disease prognosis and selects the eligible targeted therapy in personalized medicine. Restoring of healthy vaginal microflora and reversing the outbreak of cervical abnormality can be also achieved by dietary habits as well as uptake of prebiotics, probiotics, synbiotics, microbial transplantation, and others.
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Affiliation(s)
- Veronika Holubekova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, SK-03601, Slovakia
| | - Zuzana Kolkova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, SK-03601, Slovakia
| | - Ivana Kasubova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, SK-03601, Slovakia
| | - Marek Samec
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, SK-03601, Slovakia
| | - Alena Mazurakova
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin University Hospital, Martin, SK-03601, Slovakia
| | - Lenka Koklesova
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin University Hospital, Martin, SK-03601, Slovakia
| | - Peter Kubatka
- Department of Medical Biology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, SK-03601, Slovakia
| | - Tomas Rokos
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin University Hospital, Martin, SK-03601, Slovakia
| | - Erik Kozubik
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin University Hospital, Martin, SK-03601, Slovakia
| | - Kamil Biringer
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin University Hospital, Martin, SK-03601, Slovakia
| | - Erik Kudela
- Clinic of Obstetrics and Gynecology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin University Hospital, Martin, SK-03601, Slovakia
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Dupras C, Bunnik EM. Toward a Framework for Assessing Privacy Risks in Multi-Omic Research and Databases. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2021; 21:46-64. [PMID: 33433298 DOI: 10.1080/15265161.2020.1863516] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
While the accumulation and increased circulation of genomic data have captured much attention over the past decade, privacy risks raised by the diversification and integration of omics have been largely overlooked. In this paper, we propose the outline of a framework for assessing privacy risks in multi-omic research and databases. Following a comparison of privacy risks associated with genomic and epigenomic data, we dissect ten privacy risk-impacting omic data properties that affect either the risk of re-identification of research participants, or the sensitivity of the information potentially conveyed by biological data. We then propose a three-step approach for the assessment of privacy risks in the multi-omic era. Thus, we lay grounds for a data property-based, 'pan-omic' approach that moves away from genetic exceptionalism. We conclude by inviting our peers to refine these theoretical foundations, put them to the test in their respective fields, and translate our approach into practical guidance.
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Asada K, Takasawa K, Machino H, Takahashi S, Shinkai N, Bolatkan A, Kobayashi K, Komatsu M, Kaneko S, Okamoto K, Hamamoto R. Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines 2021; 9:biomedicines9111513. [PMID: 34829742 PMCID: PMC8614827 DOI: 10.3390/biomedicines9111513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/06/2021] [Accepted: 10/19/2021] [Indexed: 01/14/2023] Open
Abstract
In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Koji Okamoto
- Division of Cancer Differentiation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryuji Hamamoto
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J 2021; 19:5762-5790. [PMID: 34765093 PMCID: PMC8566780 DOI: 10.1016/j.csbj.2021.10.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/13/2022] Open
Abstract
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.
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Affiliation(s)
- Claudia Caudai
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Antonella Galizia
- CNR, Institute of Applied Mathematics and Information Technologies (IMATI), Genoa, Italy
| | - Filippo Geraci
- CNR, Institute for Informatics and Telematics (IIT), Pisa, Italy
| | - Loredana Le Pera
- CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Veronica Morea
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Emanuele Salerno
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Allegra Via
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Teresa Colombo
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
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Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021; 11:886. [PMID: 34575663 PMCID: PMC8471764 DOI: 10.3390/jpm11090886] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Akira Sakai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Kazuma Kobayashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Kaneko S, Takasawa K, Asada K, Shinkai N, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Komatsu M, Hamamoto R. Epigenetic Mechanisms Underlying COVID-19 Pathogenesis. Biomedicines 2021; 9:1142. [PMID: 34572329 PMCID: PMC8466119 DOI: 10.3390/biomedicines9091142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 12/11/2022] Open
Abstract
In 2019, a novel severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported and was declared a pandemic by the World Health Organization (WHO) in March 2020. With the advancing development of COVID-19 vaccines and their administration globally, it is expected that COVID-19 will converge in the future; however, the situation remains unpredictable because of a series of reports regarding SARS-CoV-2 variants. Currently, there are still few specific effective treatments for COVID-19, as many unanswered questions remain regarding the pathogenic mechanism of COVID-19. Continued elucidation of COVID-19 pathogenic mechanisms is a matter of global importance. In this regard, recent reports have suggested that epigenetics plays an important role; for instance, the expression of angiotensin I converting enzyme 2 (ACE2) receptor, an important factor in human infection with SARS-CoV-2, is epigenetically regulated; further, DNA methylation status is reported to be unique to patients with COVID-19. In this review, we focus on epigenetic mechanisms to provide a new molecular framework for elucidating the pathogenesis of SARS-CoV-2 infection in humans and of COVID-19, along with the possibility of new diagnostic and therapeutic strategies.
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Affiliation(s)
- Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Takasawa
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Norio Shinkai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- National Cancer Center Hospital, Department of Endoscopy, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Hidenori Machino
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (S.K.); (K.T.); (K.A.); (N.S.); (A.B.); (M.Y.); (S.T.); (H.M.); (K.K.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Liu C, Sun YV. Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics. Endocrinol Metab Clin North Am 2021; 50:559-574. [PMID: 34399961 DOI: 10.1016/j.ecl.2021.05.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.
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Affiliation(s)
- Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA; Atlanta VA Healthcare System, 1670 Clairmont Road, Decatur, GA 30033, USA.
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Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals. Cancers (Basel) 2021; 13:cancers13143611. [PMID: 34298824 PMCID: PMC8306149 DOI: 10.3390/cancers13143611] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 07/12/2021] [Accepted: 07/15/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Radiogenomics enables prediction of the status and prognosis of patients using non-invasively obtained imaging data. Current machine learning (ML) methods used in radiogenomics require huge datasets, which involve the handling of large heterogeneous datasets from multiple cohorts/hospitals. In this study, two different glioma datasets were used to test various ML and image pre-processing methods to confirm whether the models trained on one dataset are universally applicable to other datasets. Our result suggested that the ML method that yielded the highest accuracy in a single dataset was likely to be overfitted. We demonstrated that implementation of standardization and dimension reduction procedures prior to classification, enabled the development of ML methods that are less affected by the multiple cohort difference. We advocate using caution in interpreting the results of radiogenomic studies of the training and testing datasets that are small or mixed, with a view to implementing practical ML methods in radiogenomics. Abstract Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.
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Komatsu M, Sakai A, Dozen A, Shozu K, Yasutomi S, Machino H, Asada K, Kaneko S, Hamamoto R. Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines 2021; 9:720. [PMID: 34201827 PMCID: PMC8301304 DOI: 10.3390/biomedicines9070720] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/13/2021] [Accepted: 06/18/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.
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Affiliation(s)
- Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Akira Sakai
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ai Dozen
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Kanto Shozu
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Research Unit, Fujitsu Research, Fujitsu Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP—Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (H.M.); (K.A.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.D.); (K.S.)
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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Asada K, Kaneko S, Takasawa K, Machino H, Takahashi S, Shinkai N, Shimoyama R, Komatsu M, Hamamoto R. Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Front Oncol 2021; 11:666937. [PMID: 34055633 PMCID: PMC8149908 DOI: 10.3389/fonc.2021.666937] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/26/2021] [Indexed: 12/17/2022] Open
Abstract
With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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35
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The role of epigenetic modifications for the pathogenesis of Crohn's disease. Clin Epigenetics 2021; 13:108. [PMID: 33980294 PMCID: PMC8117638 DOI: 10.1186/s13148-021-01089-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/22/2021] [Indexed: 12/19/2022] Open
Abstract
Epigenetics has become a promising field for finding new biomarkers and improving diagnosis, prognosis, and drug response in inflammatory bowel disease. The number of people suffering from inflammatory bowel diseases, especially Crohn's disease, has increased remarkably. Crohn's disease is assumed to be the result of a complex interplay between genetic susceptibility, environmental factors, and altered intestinal microbiota, leading to dysregulation of the innate and adaptive immune response. While many genetic variants have been identified to be associated with Crohn's disease, less is known about the influence of epigenetics in the pathogenesis of this disease. In this review, we provide an overview of current epigenetic studies in Crohn's disease. In particular, we enable a deeper insight into applied bioanalytical and computational tools, as well as a comprehensive update toward the cell-specific evaluation of DNA methylation and histone modifications.
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Brasil S, Neves CJ, Rijoff T, Falcão M, Valadão G, Videira PA, Dos Reis Ferreira V. Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases. Front Mol Biosci 2021; 8:648012. [PMID: 34026829 PMCID: PMC8131862 DOI: 10.3389/fmolb.2021.648012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 04/09/2021] [Indexed: 12/29/2022] Open
Abstract
More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.
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Affiliation(s)
- Sandra Brasil
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Cátia José Neves
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Tatiana Rijoff
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
| | - Marta Falcão
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Gonçalo Valadão
- Instituto de Telecomunicações, Lisbon, Portugal.,Departamento de Ciências e Tecnologias, Autónoma Techlab - Universidade Autónoma de Lisboa, Lisbon, Portugal.,Electronics, Telecommunications and Computers Engineering Department, Instituto Superior de Engenharia de Lisboa, Lisbon, Portugal
| | - Paula A Videira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal.,UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Vanessa Dos Reis Ferreira
- Portuguese Association for CDG, Lisbon, Portugal.,CDG & Allies - Professionals and Patient Associations International Network (CDG & Allies - PPAIN), Caparica, Portugal
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Kaneko S, Mitsuyama T, Shiraishi K, Ikawa N, Shozu K, Dozen A, Machino H, Asada K, Komatsu M, Kukita A, Sone K, Yoshida H, Motoi N, Hayami S, Yoneoka Y, Kato T, Kohno T, Natsume T, von Keudell G, Saloura V, Yamaue H, Hamamoto R. Genome-Wide Chromatin Analysis of FFPE Tissues Using a Dual-Arm Robot with Clinical Potential. Cancers (Basel) 2021; 13:cancers13092126. [PMID: 33924956 PMCID: PMC8125448 DOI: 10.3390/cancers13092126] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 12/24/2022] Open
Abstract
Although chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) using formalin-fixed paraffin-embedded tissue (FFPE) has been reported, it remained elusive whether they retained accurate transcription factor binding. Here, we developed a method to identify the binding sites of the insulator transcription factor CTCF and the genome-wide distribution of histone modifications involved in transcriptional activation. Importantly, we provide evidence that the ChIP-seq datasets obtained from FFPE samples are similar to or even better than the data for corresponding fresh-frozen samples, indicating that FFPE samples are compatible with ChIP-seq analysis. H3K27ac ChIP-seq analyses of 69 FFPE samples using a dual-arm robot revealed that driver mutations in EGFR were distinguishable from pan-negative cases and were relatively homogeneous as a group in lung adenocarcinomas. Thus, our results demonstrate that FFPE samples are an important source for epigenomic research, enabling the study of histone modifications, nuclear chromatin structure, and clinical data.
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Affiliation(s)
- Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Correspondence: (S.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Toutai Mitsuyama
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan;
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (K.S.); (T.K.)
| | - Noriko Ikawa
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
| | - Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
| | - Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Asako Kukita
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo 113-0033, Japan; (A.K.); (K.S.)
| | - Kenbun Sone
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo 113-0033, Japan; (A.K.); (K.S.)
| | - Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (H.Y.); (N.M.)
| | - Noriko Motoi
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (H.Y.); (N.M.)
| | - Shinya Hayami
- Second Department of Surgery, School of Medicine, Wakayama Medical University, Wakayama 641-0011, Japan; (S.H.); (H.Y.)
| | - Yutaka Yoneoka
- Department of Gynecology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (Y.Y.); (T.K.)
| | - Tomoyasu Kato
- Department of Gynecology, National Cancer Center Hospital, Tokyo 104-0045, Japan; (Y.Y.); (T.K.)
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (K.S.); (T.K.)
| | - Toru Natsume
- Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, Tokyo 100-8921, Japan;
- Robotic Biology Institute, Inc., Tokyo 135-0064, Japan
| | | | - Vassiliki Saloura
- Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA;
| | - Hiroki Yamaue
- Second Department of Surgery, School of Medicine, Wakayama Medical University, Wakayama 641-0011, Japan; (S.H.); (H.Y.)
| | - Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo 104-0045, Japan; (N.I.); (K.S.); (A.D.); (H.M.); (K.A.); (M.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Correspondence: (S.K.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning. Biomolecules 2021; 11:biom11040565. [PMID: 33921457 PMCID: PMC8070530 DOI: 10.3390/biom11040565] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 02/06/2023] Open
Abstract
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.
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Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities. Cancers (Basel) 2021; 13:cancers13061415. [PMID: 33808802 PMCID: PMC8003655 DOI: 10.3390/cancers13061415] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 03/14/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary This study evaluates the performance degradation of machine learning models for segmenting gliomas in brain magnetic resonance images caused by domain shift and proposed possible solutions. Although machine learning models exhibit significant potential for clinical applications, performance degradation in different cohorts is a problem that must be solved. In this study, we identify the impact of the performance degradation of machine learning models to be significant enough to render clinical applications difficult. This demonstrates that it can be improved by fine-tuning methods with a small number of cases from each facility, although the data obtained appeared to be biased. Our method creates a facility-specific machine learning model from a small real-world dataset and public dataset; therefore, our fine-tuning method could be a practical solution in situations where only a small dataset is available. Abstract Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.
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Inoue F, Sone K, Toyohara Y, Takahashi Y, Kukita A, Hara A, Taguchi A, Tanikawa M, Tsuruga T, Osuga Y. Targeting Epigenetic Regulators for Endometrial Cancer Therapy: Its Molecular Biology and Potential Clinical Applications. Int J Mol Sci 2021; 22:2305. [PMID: 33669072 PMCID: PMC7956745 DOI: 10.3390/ijms22052305] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/09/2021] [Accepted: 02/20/2021] [Indexed: 12/26/2022] Open
Abstract
Endometrial cancer is one of the most frequently diagnosed gynecological malignancies worldwide. However, its prognosis in advanced stages is poor, and there are only few available treatment options when it recurs. Epigenetic changes in gene function, such as DNA methylation, histone modification, and non-coding RNA, have been studied for the last two decades. Epigenetic dysregulation is often reported in the development and progression of various cancers. Recently, epigenetic changes in endometrial cancer have also been discussed. In this review, we give the main points of the role of DNA methylation and histone modification in endometrial cancer, the diagnostic tools to determine these modifications, and inhibitors targeting epigenetic regulators that are currently in preclinical studies and clinical trials.
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Affiliation(s)
| | - Kenbun Sone
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8655, Japan; (F.I.); (Y.T.); (Y.T.); (A.K.); (A.H.); (A.T.); (M.T.); (T.T.); (Y.O.)
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41
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Li LR, Du B, Liu HQ, Chen C. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Front Oncol 2021; 10:604051. [PMID: 33634025 PMCID: PMC7899964 DOI: 10.3389/fonc.2020.604051] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/21/2020] [Indexed: 12/12/2022] Open
Abstract
Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.
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Affiliation(s)
- Ling-Rui Li
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Du
- School of Computer Science, Wuhan University, Wuhan, China.,Institute of Artificial Intelligence, Wuhan University, Wuhan, China
| | - Han-Qing Liu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chuang Chen
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
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42
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Application of Artificial Intelligence for Medical Research. Biomolecules 2021; 11:biom11010090. [PMID: 33445802 PMCID: PMC7828229 DOI: 10.3390/biom11010090] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 01/11/2021] [Indexed: 12/11/2022] Open
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Shozu K, Komatsu M, Sakai A, Komatsu R, Dozen A, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S, Matsuoka R, Nakashima A, Sekizawa A, Hamamoto R. Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos. Biomolecules 2020; 10:E1691. [PMID: 33348873 PMCID: PMC7766150 DOI: 10.3390/biom10121691] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/12/2020] [Accepted: 12/15/2020] [Indexed: 12/17/2022] Open
Abstract
The application of segmentation methods to medical imaging has the potential to create novel diagnostic support models. With respect to fetal ultrasound, the thoracic wall is a key structure on the assessment of the chest region for examiners to recognize the relative orientation and size of structures inside the thorax, which are critical components in neonatal prognosis. In this study, to improve the segmentation performance of the thoracic wall in fetal ultrasound videos, we proposed a novel model-agnostic method using deep learning techniques: the Multi-Frame + Cylinder method (MFCY). The Multi-frame method (MF) uses time-series information of ultrasound videos, and the Cylinder method (CY) utilizes the shape of the thoracic wall. To evaluate the achieved improvement, we performed segmentation using five-fold cross-validation on 538 ultrasound frames in the four-chamber view (4CV) of 256 normal cases using U-net and DeepLabv3+. MFCY increased the mean values of the intersection over union (IoU) of thoracic wall segmentation from 0.448 to 0.493 for U-net and from 0.417 to 0.470 for DeepLabv3+. These results demonstrated that MFCY improved the segmentation performance of the thoracic wall in fetal ultrasound videos without altering the network structure. MFCY is expected to facilitate the development of diagnostic support models in fetal ultrasound by providing further accurate segmentation of the thoracic wall.
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Affiliation(s)
- Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Department of Obstetrics and Gynecology, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan;
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Akira Sakai
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-Ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Reina Komatsu
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Suguru Yasutomi
- Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-Ku, Kawasaki, Kanagawa 211-8588, Japan; (A.S.); (S.Y.)
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
| | - Tatsuya Arakaki
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ryu Matsuoka
- RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (R.K.); (R.M.)
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Akitoshi Nakashima
- Department of Obstetrics and Gynecology, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan;
| | - Akihiko Sekizawa
- Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo 142-8666, Japan; (T.A.); (A.S.)
| | - Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.S.); (A.D.); (H.M.); (K.A.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
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Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A, Takasawa K, Bolatkan A, Shozu K, Dozen A, Machino H, Takahashi S, Asada K, Komatsu M, Sese J, Kaneko S. Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers (Basel) 2020; 12:E3532. [PMID: 33256107 PMCID: PMC7760590 DOI: 10.3390/cancers12123532] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 11/21/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.
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Affiliation(s)
- Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Kruthi Suvarna
- Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India;
| | - Masayoshi Yamada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku Tokyo 104-0045, Japan
| | - Kazuma Kobayashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Norio Shinkai
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Mototaka Miyake
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Masamichi Takahashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Shunichi Jinnai
- Department of Dermatologic Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryo Shimoyama
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Akira Sakai
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ken Takasawa
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Amina Bolatkan
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Satoshi Takahashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Jun Sese
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Humanome Lab, 2-4-10 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
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Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data. Biomolecules 2020; 10:biom10101460. [PMID: 33086649 PMCID: PMC7603376 DOI: 10.3390/biom10101460] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/13/2020] [Accepted: 10/18/2020] [Indexed: 12/13/2022] Open
Abstract
Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.
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Jinnai S, Yamazaki N, Hirano Y, Sugawara Y, Ohe Y, Hamamoto R. The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning. Biomolecules 2020; 10:biom10081123. [PMID: 32751349 PMCID: PMC7465007 DOI: 10.3390/biom10081123] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/25/2020] [Accepted: 07/28/2020] [Indexed: 12/13/2022] Open
Abstract
Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.
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Affiliation(s)
- Shunichi Jinnai
- Department of Dermatologic Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
- Correspondence: (S.J.); (R.H.)
| | - Naoya Yamazaki
- Department of Dermatologic Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Yuichiro Hirano
- Preferred Networks, 1-6-1 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan; (Y.H.); (Y.S.)
| | - Yohei Sugawara
- Preferred Networks, 1-6-1 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan; (Y.H.); (Y.S.)
| | - Yuichiro Ohe
- Department of Thoracic Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Correspondence: (S.J.); (R.H.)
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Dulman RS, Wandling GM, Pandey SC. Epigenetic mechanisms underlying pathobiology of alcohol use disorder. CURRENT PATHOBIOLOGY REPORTS 2020; 8:61-73. [PMID: 33747641 DOI: 10.1007/s40139-020-00210-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Purpose of review Chronic alcohol use is a worldwide problem with multifaceted consequences including multiplying medical costs and sequelae, societal effects like drunk driving and assault, and lost economic productivity. These large-scale outcomes are driven by the consumption of ethanol, a small permeable molecule that has myriad effects in the human body, particularly in the liver and brain. In this review, we have summarized effects of acute and chronic alcohol consumption on epigenetic mechanisms that may drive pathobiology of Alcohol Use Disorder (AUD) while identifying areas of need for future research. Recent findings Epigenetics has emerged as an interesting field of biology at the intersection of genetics and the environment, and ethanol in particular has been identified as a potent modulator of the epigenome with various effects on DNA methylation, histone modifications, and non-coding RNAs. These changes alter chromatin dynamics and regulate gene expression that contribute to behavioral and physiological changes leading to the development of AUD psychopathology and cancer pathology. Summary Evidence and discussion presented here from preclinical results and available translational studies have increased our knowledge of the epigenetic effects of alcohol consumption. These studies have identified targets that can be used to develop better therapies to reduce chronic alcohol abuse and mitigate its societal burden and pathophysiology.
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Affiliation(s)
- Russell S Dulman
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Gabriela M Wandling
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Subhash C Pandey
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA.,Jesse Brown VA Medical Center, Chicago, IL 60612, USA
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48
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Coulouarn C. Artificial intelligence and omics in cancer. Artif Intell Cancer 2020; 1:1-7. [DOI: 10.35713/aic.v1.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/09/2020] [Accepted: 06/12/2020] [Indexed: 02/06/2023] Open
Abstract
Cancer is a major public health problem worldwide. Current predictions suggest that 13 million people will die each year from cancer by 2030. Thus, new ideas are urgently needed to change paradigms in the global fight against cancer. Over the last decades, artificial intelligence (AI) emerged in the field of cancer research as a new and promising discipline. Although emerging, a great potential is appreciated in AI to improve cancer diagnosis and prognosis, as well as to identify relevant therapeutics in the current era of personalized medicine. Developing pipelines connecting patient-generated health data easily translatable into clinical practice to assist clinicians in decision making represents a challenging but fascinating task. AI algorithms are mainly fueled by multi omics data which, in the case of cancer research, have been largely derived from international cancer programs, including The Cancer Genome Atlas (TCGA). Here, I briefly review some examples of supervised and unsupervised big data derived from TCGA programs and comment on how AI algorithms have been applied to improve the management of patients with cancer. In this context, Artificial Intelligence in Cancer journal was specifically launched to promote the development of this discipline, by serving as a forum to publish high-quality basic and clinical research articles in various fields of AI in oncology.
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Affiliation(s)
- Cédric Coulouarn
- Institut National de la Sante et de la Recherche Medicale (Inserm), Université de Rennes 1, Rennes F-35000, France
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49
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Jaiswal R, Jafa E. Epigenetics. Indian J Med Paediatr Oncol 2020. [DOI: 10.4103/ijmpo.ijmpo_24_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
AbstractHistorically, cancer is known to be a genetic disease. It is now realized that it involves epigenetic abnormalities along with genetic alterations. Epigenetics is an extra layer of instruction that lies upon DNA and controls how the genes are read and expressed. It simply means changes in phenotype without a change in genotype. In this review we aim to discuss the fundamentals of epigenetics, role of these alterations in carcinogenesis and its implications for epigenetic therapies.
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Affiliation(s)
- Ravi Jaiswal
- Department of Medical Oncology, BIACHRI, Hyderabad, Telangana, India
| | - Esha Jafa
- Department of Medical Oncology, Superspeciality Cancer Centre, Lucknow, Uttar Pradesh, India
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Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer. Biomolecules 2020; 10:biom10040524. [PMID: 32235589 PMCID: PMC7225957 DOI: 10.3390/biom10040524] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 03/25/2020] [Accepted: 03/27/2020] [Indexed: 12/17/2022] Open
Abstract
Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.
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