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Sarumi OA, Hahn M, Heider D. NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search. Comput Struct Biotechnol J 2024; 23:732-741. [PMID: 38298179 PMCID: PMC10828564 DOI: 10.1016/j.csbj.2023.12.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/28/2023] [Accepted: 12/28/2023] [Indexed: 02/02/2024] Open
Abstract
The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens. Improving DNA similarity search is of outmost importance because of the increased complexity of the evergrowing repositories of sequences. Therefore, instead of using the conventional approach of comparing raw sequences, e.g., in fasta format, a numerical representation of the sequences can be used to calculate their similarities and optimize the search process. In this study, we analyzed different approaches for numerical embeddings, including Chaos Game Representation, hashing, and neural networks, and compared them with classical approaches such as principal component analysis. It turned out that neural networks generate embeddings that are able to capture the similarity between DNA sequences as a distance measure and outperform the other approaches on DNA similarity search, significantly.
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Affiliation(s)
- Oluwafemi A. Sarumi
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg, D-35043, Germany
- Institute of Computer Science, Heinrich-Heine-University Duesseldorf, Graf-Adolf-Str. 63, Duesseldorf, D-40215, Germany
| | - Maximilian Hahn
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg, D-35043, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg, D-35043, Germany
- Institute of Computer Science, Heinrich-Heine-University Duesseldorf, Graf-Adolf-Str. 63, Duesseldorf, D-40215, Germany
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Pergande MR, Osterbauer KJ, Buck KM, Roberts DS, Wood NN, Balasubramanian P, Mann MW, Rossler KJ, Diffee GM, Colman RJ, Anderson RM, Ge Y. Mass Spectrometry-Based Multiomics Identifies Metabolic Signatures of Sarcopenia in Rhesus Monkey Skeletal Muscle. J Proteome Res 2024; 23:2845-2856. [PMID: 37991985 PMCID: PMC11109024 DOI: 10.1021/acs.jproteome.3c00474] [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] [Indexed: 11/24/2023]
Abstract
Sarcopenia is a progressive disorder characterized by age-related loss of skeletal muscle mass and function. Although significant progress has been made over the years to identify the molecular determinants of sarcopenia, the precise mechanisms underlying the age-related loss of contractile function remains unclear. Advances in "omics" technologies, including mass spectrometry-based proteomic and metabolomic analyses, offer great opportunities to better understand sarcopenia. Herein, we performed mass spectrometry-based analyses of the vastus lateralis from young, middle-aged, and older rhesus monkeys to identify molecular signatures of sarcopenia. In our proteomic analysis, we identified proteins that change with age, including those involved in adenosine triphosphate and adenosine monophosphate metabolism as well as fatty acid beta oxidation. In our untargeted metabolomic analysis, we identified metabolites that changed with age largely related to energy metabolism including fatty acid beta oxidation. Pathway analysis of age-responsive proteins and metabolites revealed changes in muscle structure and contraction as well as lipid, carbohydrate, and purine metabolism. Together, this study discovers new metabolic signatures and offers new insights into the molecular mechanisms underlying sarcopenia for the evaluation and monitoring of a therapeutic treatment of sarcopenia.
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Affiliation(s)
- Melissa R. Pergande
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Katie J. Osterbauer
- Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Kevin M. Buck
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - David S. Roberts
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Nina N. Wood
- Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Morgan W. Mann
- Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Kalina J. Rossler
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Gary M. Diffee
- Department of Kinesiology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ricki J. Colman
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI 53705, USA
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Rozalyn M. Anderson
- Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI 53705, USA
| | - Ying Ge
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Medicine, University of Wisconsin-Madison, Madison, WI 53705, USA
- Human Proteomics Program, University of Wisconsin-Madison, Madison, WI 53705, USA
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3
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Li B, Liu H, Zhao M, Zhang X, Huang P, Chen X, Lin J. Carboxylesterase Activatable Molecular Probe for Personalized Treatment Guidance by Analyte-Induced Molecular Transformation. Angew Chem Int Ed Engl 2024; 63:e202404093. [PMID: 38727540 DOI: 10.1002/anie.202404093] [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: 02/28/2024] [Indexed: 06/28/2024]
Abstract
Accurate visualization of tumor microenvironment is of great significance for personalized medicine. Here, we develop a near-infrared (NIR) fluorescence/photoacoustic (FL/PA) dual-mode molecular probe (denoted as NIR-CE) for distinguishing tumors based on carboxylesterase (CE) level by an analyte-induced molecular transformation (AIMT) strategy. The recognition moiety for CE activity is the acetyl unit of NIR-CE, generating the pre-product, NIR-CE-OH, which undergoes spontaneous hydrogen atom exchange between the nitrogen atoms in the indole group and the phenol hydroxyl group, eventually transforming into NIR-CE-H. In cellular experiments and in vivo blind studies, the human hepatoma cells and tumors with high level of CE were successfully distinguished by both NIR FL and PA imaging. Our findings provide a new molecular imaging strategy for personalized treatment guidance.
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Affiliation(s)
- Benhao Li
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and Faculty of Engineering, National University of Singapore, Singapore, 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
- Nanomedicine Translational Research Program, NUS Centre for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
| | - Hengke Liu
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
| | - Mengyao Zhao
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and Faculty of Engineering, National University of Singapore, Singapore, 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
- Nanomedicine Translational Research Program, NUS Centre for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
| | - Xinming Zhang
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
| | - Peng Huang
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
| | - Xiaoyuan Chen
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and Faculty of Engineering, National University of Singapore, Singapore, 119074, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore
- Nanomedicine Translational Research Program, NUS Centre for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Theranostics Center of Excellence (TCE), Yong Loo Lin School of Medicine, National University of Singapore, 11 Biopolis Way, Helios, Singapore, 138667
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore
| | - Jing Lin
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
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4
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Wang R, Huang S, Wang P, Shi X, Li S, Ye Y, Zhang W, Shi L, Zhou X, Tang X. Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023. Cancer Imaging 2024; 24:85. [PMID: 38965599 PMCID: PMC11223420 DOI: 10.1186/s40644-024-00737-0] [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: 02/21/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Recently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer. METHODS We retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords. RESULTS We found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer. CONCLUSIONS Overall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.
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Affiliation(s)
- Ruiyu Wang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Shu Huang
- Department of Gastroenterology, Lianshui County People' Hospital, Huaian, China
- Department of Gastroenterology, Lianshui People' Hospital of Kangda CollegeAffiliated to, Nanjing Medical University , Huaian, China
| | - Ping Wang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Xiaomin Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Shiqi Li
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Yusong Ye
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Wei Zhang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Lei Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China.
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China.
| | - Xiaowei Tang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, Sichuan Province, 646099, China.
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China.
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Liu Q, Zheng J, Xie A, Chen M, Gong RY, Sheng Y, Chen HL, Qi CB. Exosome, a Rising Biomarkers in Liquid Biopsy: Advances of Label-Free and Label Strategy for Diagnosis of Cancer. Crit Rev Anal Chem 2024:1-12. [PMID: 38669199 DOI: 10.1080/10408347.2024.2339961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Cancer is commonly considered as one of the most severe diseases, posing a significant threat to human health and society due to various serious challenges. These challenges include difficulties in accurate diagnosis and a high propensity to form metastasis. Tissue biopsy remains the gold standard for diagnosing and subtyping cancer. However, concerns arise from its invasive nature and the potential risk of metastasis during these complex diagnostic procedures. Meanwhile, liquid biopsy has recently witnessed the rapid advancements with the emergence of three prominent detection biomarkers: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Whereas, the very low abundance of CTCs combined with the instability of ctDNA intensify the challenges and decrease the accuracy of these two biomarkers for cancer diagnosis. While exosomes have gained widespread recognition as a promising biomarker in liquid biopsy due to their relatively low-invasive detection method, excellent biostability, rich resources, high abundance, and ability to provide valuable information about cancer. Therefore, it is crucial to systematically summarize recent advancements mainly in exosome-based detection methods for early cancer diagnosis. Specifically, this review will primarily focus on label-based and label-free strategies for detecting cancer using exosomes. We anticipate that this comprehensive analysis will enhance readers' understanding of the significance and value of exosomes in the fields of cancer diagnosis and therapy.
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Affiliation(s)
- Qian Liu
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Jing Zheng
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - An Xie
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Min Chen
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Rui-Yue Gong
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Yuan Sheng
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Hong-Lei Chen
- Department of Pathology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Chu-Bo Qi
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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Rakhshaninejad M, Fathian M, Shirkoohi R, Barzinpour F, Gandomi AH. Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach. BMC Bioinformatics 2024; 25:33. [PMID: 38253993 PMCID: PMC10810249 DOI: 10.1186/s12859-024-05657-1] [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: 12/03/2023] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.
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Affiliation(s)
- Morteza Rakhshaninejad
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Mohammad Fathian
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran.
| | - Reza Shirkoohi
- Cancer Biology Research Center, Cancer Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Keshavarz Boulevard, Tehran, 1419733141, Tehran, Iran
| | - Farnaz Barzinpour
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary
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7
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Herr J, Stoyanova R, Mellon EA. Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review. Crit Rev Oncog 2024; 29:33-65. [PMID: 38683153 DOI: 10.1615/critrevoncog.2023050852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.
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Affiliation(s)
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| | - Eric Albert Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
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8
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Danishuddin, Khan S, Kim JJ. From cancer big data to treatment: Artificial intelligence in cancer research. J Gene Med 2024; 26:e3629. [PMID: 37940369 DOI: 10.1002/jgm.3629] [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: 08/25/2023] [Revised: 09/12/2023] [Accepted: 10/18/2023] [Indexed: 11/10/2023] Open
Abstract
In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.
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Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
| | - Shawez Khan
- National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
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9
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Guo W, Lv C, Guo M, Zhao Q, Yin X, Zhang L. Innovative applications of artificial intelligence in zoonotic disease management. SCIENCE IN ONE HEALTH 2023; 2:100045. [PMID: 39077042 PMCID: PMC11262289 DOI: 10.1016/j.soh.2023.100045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/22/2023] [Indexed: 07/31/2024]
Abstract
Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.
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Affiliation(s)
- Wenqiang Guo
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chenrui Lv
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Meng Guo
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450046, China
| | - Qiwei Zhao
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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10
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Blutt SE, Coarfa C, Neu J, Pammi M. Multiomic Investigations into Lung Health and Disease. Microorganisms 2023; 11:2116. [PMID: 37630676 PMCID: PMC10459661 DOI: 10.3390/microorganisms11082116] [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: 07/12/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.
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Affiliation(s)
- Sarah E. Blutt
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA;
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Cristian Coarfa
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Josef Neu
- Department of Pediatrics, Section of Neonatology, University of Florida, Gainesville, FL 32611, USA;
| | - Mohan Pammi
- Department of Pediatrics, Section of Neonatology, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX 77030, USA
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