1
|
Duenas S, McGee Z, Mhatre I, Mayilvahanan K, Patel KK, Abdelhalim H, Jayprakash A, Wasif U, Nwankwo O, Degroat W, Yanamala N, Sengupta PP, Fine D, Ahmed Z. Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine. Hum Genomics 2024; 18:116. [PMID: 39427205 PMCID: PMC11491019 DOI: 10.1186/s40246-024-00685-7] [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: 04/06/2024] [Accepted: 10/14/2024] [Indexed: 10/21/2024] Open
Abstract
Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.
Collapse
Affiliation(s)
- Sophia Duenas
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Zachary McGee
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Ishani Mhatre
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Karthikeyan Mayilvahanan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Kush Ketan Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Atharv Jayprakash
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Uzayr Wasif
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Oluchi Nwankwo
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - William Degroat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiovascular Diseases and Hypertension, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
| | - Partho P Sengupta
- Division of Cardiovascular Diseases and Hypertension, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
- Department of Medicine, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA
| | - Daniel Fine
- Department of Oral Biology, Rutgers School of Dental Medicine, 110 Bergen Street, Newark, NJ, US
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA.
- Division of Cardiovascular Diseases and Hypertension, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA.
- Department of Medicine, Rutgers Biomedical and Health Sciences, Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ, USA.
| |
Collapse
|
2
|
Dasgupta S. Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:492-503. [PMID: 39269895 DOI: 10.1089/omi.2024.0150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease (GERD) pose a significant burden on planetary health, but also the coexistence of GERD in patients with LC is often associated with a poor prognosis. This study reports on the genetic overlaps between these two conditions using systems biology-driven bioinformatics and machine learning-based algorithms. A total of nine hub genes including IGHV1-3, COL3A1, ITGA11, COL1A1, MS4A1, SPP1, MMP9, MMP7, and LOC102723407 were found to be significantly altered in both LC and GERD as compared with controls and with pathway analyses suggesting a significant association with the matrix remodeling pathway. The expression of these genes was validated in two additional datasets. Random forest and K-nearest neighbor, two machine learning-based algorithms, achieved accuracies of 89% and 85% for distinguishing LC and GERD, respectively, from controls using these hub genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of doxycycline to matrix metalloproteinase 7 (binding affinity: -6.8 kcal/mol). The present study is the first of its kind that combines in silico and machine learning algorithms to identify the gene signatures that relate to both LC and GERD and promising drug candidates that warrant further research in relation to therapeutic innovation in LC and GERD. Finally, this study also suggests upstream regulators, including microRNAs and transcription factors, that can inform future mechanistic research on LC and GERD.
Collapse
Affiliation(s)
- Sanjukta Dasgupta
- Department of Biotechnology, Center for Multidisciplinary Research and Innovations, Brainware University, Barasat, India
| |
Collapse
|
3
|
Brahimllari O, Eloranta S, Georgii-Hemming P, Haider Z, Koch S, Krstic A, Skarp FP, Rosenquist R, Smedby KE, Taylan F, Thorvaldsdottir B, Wirta V, Wästerlid T, Boman M. Smart variant filtering - A blueprint solution for massively parallel sequencing-based variant analysis. Health Informatics J 2024; 30:14604582241290725. [PMID: 39394057 DOI: 10.1177/14604582241290725] [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: 10/13/2024]
Abstract
Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification's accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic.
Collapse
Affiliation(s)
- Orlinda Brahimllari
- MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Sandra Eloranta
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Zahra Haider
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Sabine Koch
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
| | - Aleksandra Krstic
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | | | - Richard Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Karin E Smedby
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Fulya Taylan
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Solna, Stockholm, Sweden
| | - Birna Thorvaldsdottir
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Valtteri Wirta
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Genomic Medicine Center Karolinska, Karolinska University Hospital, Stockholm, Sweden
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Tove Wästerlid
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Magnus Boman
- MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
4
|
Shams A. Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment. Diagnostics (Basel) 2024; 14:2174. [PMID: 39410578 PMCID: PMC11476216 DOI: 10.3390/diagnostics14192174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Revised: 09/17/2024] [Accepted: 09/23/2024] [Indexed: 10/20/2024] Open
Abstract
BACKGROUND Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. METHODOLOGY We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning. RESULTS The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. CONCLUSIONS Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.
Collapse
Affiliation(s)
- Anwar Shams
- Department of Pharmacology, College of Medicine, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; or ; Tel.: +00966-548638099
- Research Center for Health Sciences, Deanship of Graduate Studies and Scientific Research, Taif University, Taif 26432, Saudi Arabia
- High Altitude Research Center, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| |
Collapse
|
5
|
Drăgoi CM, Nicolae AC, Dumitrescu IB. Emerging Strategies in Drug Development and Clinical Care in the Era of Personalized and Precision Medicine. Pharmaceutics 2024; 16:1107. [PMID: 39204452 PMCID: PMC11359044 DOI: 10.3390/pharmaceutics16081107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 07/24/2024] [Indexed: 09/04/2024] Open
Abstract
In the ever-changing landscape of modern medicine, we face an important moment where the interplay of disease, drugs, and patients defines a new paradigm [...].
Collapse
Affiliation(s)
| | - Alina Crenguța Nicolae
- Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, 020956 Bucharest, Romania; (C.M.D.); (I.-B.D.)
| | | |
Collapse
|
6
|
Farcas AO, Stoica MC, Maier IM, Maier AC, Sin AI. Heart Transplant Rejection: From the Endomyocardial Biopsy to Gene Expression Profiling. Biomedicines 2024; 12:1926. [PMID: 39200392 PMCID: PMC11351478 DOI: 10.3390/biomedicines12081926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Revised: 08/13/2024] [Accepted: 08/20/2024] [Indexed: 09/02/2024] Open
Abstract
Heart transplant prolongs life for patients with end-stage heart failure but rejection remains a complication that reduces long-term survival. The aim is to provide a comprehensive overview of the current status in HT rejection. EMB is an invasive diagnostic tool, consisting in the sampling of a fragment of myocardial tissue from the right ventricular septum using fluoroscopic guidance. This tissue can later be subjected to histopathological, immunohistochemical or molecular analysis, providing valuable information for cardiac allograft rejection, but this procedure is not without complications. To increase the accuracy of the rejection diagnosis, EMB requires a systematic evaluation of endocardium, myocardium, interstitium and intramural vessels. There are three types of rejection: hyperacute, acute or chronic, diagnosed by the histopathological evaluation of EMB as well as by new diagnostic methods such as DSA, ddcfDNA and gene expression profiling, the last having a high negative predictive value. More than 50 years after the introduction of EMB in medical practice, it still remains the "gold standard" in monitoring rejection in HT recipients but other new, less invasive diagnostic methods reduce the number of EMBs required.
Collapse
Affiliation(s)
- Anca Otilia Farcas
- Doctoral School of Medicine and Pharmacy, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, 540142 Targu Mures, Romania;
- Department of Cell Biology, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, 540139 Targu Mures, Romania;
| | - Mihai Ciprian Stoica
- Department of Nephrology/Internal Medicine, Mures County Clinical Hospital, 540103 Targu Mures, Romania
- Department of Internal Medicine, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, 540139 Targu Mures, Romania
| | | | - Adrian Cornel Maier
- Emergency Military Hospital, 800150 Galati, Romania;
- Faculty of Medicine and Pharmacy, Dunarea de Jos University, 800008 Galati, Romania
| | - Anca Ileana Sin
- Department of Cell Biology, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, 540139 Targu Mures, Romania;
- Department of Pathology, Clinical County Emergency Hospital, 540136 Targu Mures, Romania
| |
Collapse
|
7
|
Narayanan R, DeGroat W, Mendhe D, Abdelhalim H, Ahmed Z. IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine. Biol Methods Protoc 2024; 9:bpae040. [PMID: 38884000 PMCID: PMC11176709 DOI: 10.1093/biomethods/bpae040] [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: 04/17/2024] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.
Collapse
Affiliation(s)
- Rishabh Narayanan
- Rutgers Institute for Health, Health Care Policy and Aging Research, The State University of New Jersey, New Brunswick, 08901, NJ, United States
| | - William DeGroat
- Rutgers Institute for Health, Health Care Policy and Aging Research, The State University of New Jersey, New Brunswick, 08901, NJ, United States
| | - Dinesh Mendhe
- Rutgers Institute for Health, Health Care Policy and Aging Research, The State University of New Jersey, New Brunswick, 08901, NJ, United States
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, The State University of New Jersey, New Brunswick, 08901, NJ, United States
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, The State University of New Jersey, New Brunswick, 08901, NJ, United States
- Department of Medicine, Division of Cardiovascular Disease and Hypertension, Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, United States
| |
Collapse
|
8
|
Ahammad I, Lamisa AB, Bhattacharjee A, Jamal TB, Arefin MS, Chowdhury ZM, Hossain MU, Das KC, Keya CA, Salimullah M. AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature. Brief Bioinform 2024; 25:bbae291. [PMID: 38877887 PMCID: PMC11179120 DOI: 10.1093/bib/bbae291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 05/23/2024] [Accepted: 06/04/2024] [Indexed: 06/18/2024] Open
Abstract
Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.
Collapse
Affiliation(s)
- Ishtiaque Ahammad
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Anika Bushra Lamisa
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Arittra Bhattacharjee
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Tabassum Binte Jamal
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Md Shamsul Arefin
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Zeshan Mahmud Chowdhury
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Mohammad Uzzal Hossain
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Keshob Chandra Das
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Chaman Ara Keya
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Md Salimullah
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| |
Collapse
|
9
|
Kim J, Seok J. ctGAN: combined transformation of gene expression and survival data with generative adversarial network. Brief Bioinform 2024; 25:bbae325. [PMID: 38980369 PMCID: PMC11232285 DOI: 10.1093/bib/bbae325] [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: 02/19/2024] [Revised: 05/29/2024] [Accepted: 06/21/2024] [Indexed: 07/10/2024] Open
Abstract
Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.
Collapse
Affiliation(s)
- Jaeyoon Kim
- School of Electrical and Computer Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Junhee Seok
- School of Electrical and Computer Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| |
Collapse
|
10
|
彭 威, 张 泽, 肖 云. [Research progress on bioinformatics in pulmonary arterial hypertension]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2024; 26:425-431. [PMID: 38660909 PMCID: PMC11057300 DOI: 10.7499/j.issn.1008-8830.2310076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024]
Abstract
Pulmonary arterial hypertension (PAH) is a severe disease characterized by abnormal pulmonary vascular remodeling and increased right ventricular pressure load, posing a significant threat to patient health. While some pathological mechanisms of PAH have been revealed, the deeper mechanisms of pathogenesis remain to be elucidated. In recent years, bioinformatics has provided a powerful tool for a deeper understanding of the complex mechanisms of PAH through the integration of techniques such as multi-omics analysis, artificial intelligence, and Mendelian randomization. This review focuses on the bioinformatics methods and technologies used in PAH research, summarizing their current applications in the study of disease mechanisms, diagnosis, and prognosis assessment. Additionally, it analyzes the existing challenges faced by bioinformatics and its potential applications in the clinical and basic research fields of PAH in the future.
Collapse
Affiliation(s)
| | - 泽盈 张
- 中南大学湘雅二医院心血管内科,湖南长沙410007
| | | |
Collapse
|
11
|
Okwori M, Eslami A. Feature engineering from meta-data for prediction of differentially expressed genes: An investigation of Mus musculus exposed to space-conditions. Comput Biol Chem 2024; 109:108026. [PMID: 38335853 DOI: 10.1016/j.compbiolchem.2024.108026] [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: 07/14/2023] [Revised: 12/29/2023] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) when exposed to conditions in space from a set of diverse engineered features. To do this, we collected DEGs and non-differentially expressed genes (NDEGs) of Mus musculus-based experiments on the GeneLab database. We engineered a diverse set of features from factors reported in the literature to affect gene expression. An extreme gradient boosting (XGBoost) model was trained to predict if a given gene would be differentially expressed at various levels of differential expression. The test results on a separate holdout dataset showed an area under the receiver operating characteristics curves (AUCs) of 0.90±0.07, averaged across the five selected percentages of the most and least differentially expressed genes. Subsequently, we investigated the impact of selection of features, both individually with a correlation-based feature-selection procedure and in groups with a combination procedure, on the prediction performance. The feature selection confirmed some known drivers of adaptation to radiation and highlighted some new transcription factors and micro RNAs (miRNAs). Finally, gene ontology (GO) analysis revealed biological processes that tend to have expression patterns most suitable for this approach. This work highlights the potential of detection of differentially expressed genes using a machine learning (ML) approach, and provides some evidence of gene expression changes being captured by a diverse feature set not related to the condition under study.
Collapse
Affiliation(s)
- Michael Okwori
- Department of Electrical, Computer and Biomedical Engineering, Union College, Schenectady, 12308, NY, United States of America.
| | - Ali Eslami
- Department of Electrical and Computer Engineering, Wichita State University, Wichita, 67260, KS, United States of America
| |
Collapse
|
12
|
Gallo E. The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design. J Biomed Sci 2024; 31:29. [PMID: 38491519 PMCID: PMC10943851 DOI: 10.1186/s12929-024-01018-5] [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: 10/16/2023] [Accepted: 03/05/2024] [Indexed: 03/18/2024] Open
Abstract
Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.
Collapse
Affiliation(s)
- Eugenio Gallo
- Department of Medicinal Chemistry, Avance Biologicals, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- Department of Protein Engineering, RevivAb, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
| |
Collapse
|
13
|
Visan AI, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life (Basel) 2024; 14:233. [PMID: 38398742 PMCID: PMC10890405 DOI: 10.3390/life14020233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.
Collapse
Affiliation(s)
| | - Irina Negut
- National Institute for Lasers, Plasma and Radiation Physics, 409 Atomistilor Street, 077125 Magurele, Ilfov, Romania;
| |
Collapse
|
14
|
Gallo E. Current advancements in B-cell receptor sequencing fast-track the development of synthetic antibodies. Mol Biol Rep 2024; 51:134. [PMID: 38236361 DOI: 10.1007/s11033-023-08941-0] [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: 10/07/2023] [Accepted: 11/13/2023] [Indexed: 01/19/2024]
Abstract
Synthetic antibodies (Abs) are a class of engineered proteins designed to mimic the functions of natural Abs. These are produced entirely in vitro, eliminating the need for an immune response. As such, synthetic Abs have transformed the traditional methods of raising Abs. Likewise, deep sequencing technologies have revolutionized genomics and molecular biology. These enable the rapid and cost-effective sequencing of DNA and RNA molecules. They have allowed for accurate and inexpensive analysis of entire genomes and transcriptomes. Notably, via deep sequencing it is now possible to sequence a person's entire B-cell receptor immune repertoire, termed BCR sequencing. This procedure allows for big data explorations of natural Abs associated with an immune response. Importantly, the identified sequences have the ability to improve the design and engineering of synthetic Abs by offering an initial sequence framework for downstream optimizations. Additionally, machine learning algorithms can be introduced to leverage the vast amount of BCR sequencing datasets to rapidly identify patterns hidden in big data to effectively make in silico predictions of antigen selective synthetic Abs. Thus, the convergence of BCR sequencing, machine learning, and synthetic Ab development has effectively promoted a new era in Ab therapeutics. The combination of these technologies is driving rapid advances in precision medicine, diagnostics, and personalized treatments.
Collapse
Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
| |
Collapse
|
15
|
DeGroat W, Abdelhalim H, Patel K, Mendhe D, Zeeshan S, Ahmed Z. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Sci Rep 2024; 14:1. [PMID: 38167627 PMCID: PMC10762256 DOI: 10.1038/s41598-023-50600-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.
Collapse
Affiliation(s)
- William DeGroat
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Habiba Abdelhalim
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Kush Patel
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Dinesh Mendhe
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Zeeshan Ahmed
- Health Care Policy and Aging Research, Rutgers Institute for Health, Rutgers University, 112 Paterson St, New Brunswick, NJ, 08901, USA.
- Department of Medicine/Cardiovascular Disease and Hypertension, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
| |
Collapse
|
16
|
Ahmed Z, Degroat W, Abdelhalim H, Zeeshan S, Fine D. Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches. Clin Oral Investig 2024; 28:52. [PMID: 38163819 DOI: 10.1007/s00784-023-05406-3] [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: 06/29/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVES Periodontal diseases are chronic, inflammatory disorders that involve the destruction of supporting tissues surrounding the teeth which leads to permanent damage and substantially heightens systemic exposure. If left untreated, dental, oral, and craniofacial diseases (DOCs), especially periodontitis, can increase an individual's risk in developing complex traits including cardiovascular diseases (CVDs). In this study, we are focused on systematically investigating causality between periodontitis with CVDs with the application of artificial intelligence (AI), machine learning (ML) algorithms, and state-of-the-art bioinformatics approaches using RNA-seq-driven gene expression data of CVD patients. MATERIALS AND METHODS In this study, we built a cohort of CVD patients, collected their blood samples, and performed RNA-seq and gene expression analysis to generate transcriptomic profiles. We proposed a nexus of AI/ML approaches for the identification of significant biomarkers, and predictive analysis. We implemented recursive feature elimination, Pearson correlation, chi-square, and analysis of variance to detect significant biomarkers, and utilized random forest and support vector machines for predictive analysis. RESULTS Our AI/ML analyses have led us to the preliminary conclusion that GAS5, GPX1, HLA-B, and SNHG6 are the potential gene markers that can be used to explain the causal relationship between periodontitis and CVDs. CONCLUSIONS CVDs are relatively common in patients with periodontal disease, and an increased risk of CVD is associated with periodontal disease independent of gender. Genetic susceptibility contributing to periodontitis and CVDs have been suggested to some extent, based on the similar degree of heritability shared between both complex diseases.
Collapse
Affiliation(s)
- Zeeshan Ahmed
- Department of Medicine/Cardiovascular Disease and Hypertension, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA.
| | - William Degroat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Saman Zeeshan
- Department of Radiation Oncology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, USA
| | - Daniel Fine
- Department of Oral Biology, Rutgers School of Dental Medicine, 110 Bergen Street, Newark, NJ, USA
| |
Collapse
|
17
|
Jamialahmadi H, Khalili-Tanha G, Nazari E, Rezaei-Tavirani M. Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches. GASTROENTEROLOGY AND HEPATOLOGY FROM BED TO BENCH 2024; 17:241-252. [PMID: 39308539 PMCID: PMC11413381 DOI: 10.22037/ghfbb.v17i3.2977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 05/11/2024] [Indexed: 09/25/2024]
Abstract
The incorporation of AI models into bioinformatics has brought about a revolutionary era in the analysis and interpretation of biological data. This mini-review offers a succinct overview of the indispensable role AI plays in the convergence of computational techniques and biological research. The search strategy followed PRISMA guidelines, encompassing databases such as PubMed, Embase, and Google Scholar to include studies published between 2018 and 2024, utilizing specific keywords. We explored the diverse applications of AI methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), across various domains of bioinformatics. These domains encompass genome sequencing, protein structure prediction, drug discovery, systems biology, personalized medicine, imaging, signal processing, and text mining. AI algorithms have exhibited remarkable efficacy in tackling intricate biological challenges, spanning from genome sequencing to protein structure prediction, and from drug discovery to personalized medicine. In conclusion, this study scrutinizes the evolving landscape of AI-driven tools and algorithms, emphasizing their pivotal role in expediting research, facilitating data interpretation, and catalyzing innovations in biomedical sciences.
Collapse
Affiliation(s)
- Hamid Jamialahmadi
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- These authors equally contributed to this study as the first authors.
| | - Ghazaleh Khalili-Tanha
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- These authors equally contributed to this study as the first authors.
| | - Elham Nazari
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Rezaei-Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| |
Collapse
|
18
|
DeGroat W, Mendhe D, Bhusari A, Abdelhalim H, Zeeshan S, Ahmed Z. IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics 2023; 39:btad755. [PMID: 38096588 PMCID: PMC10739559 DOI: 10.1093/bioinformatics/btad755] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
SUMMARY In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.e. Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. AVAILABILITY AND IMPLEMENTATION The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1).
Collapse
Affiliation(s)
- William DeGroat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, United States
| | - Dinesh Mendhe
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, United States
| | - Atharva Bhusari
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, United States
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, United States
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, United States
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, United States
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers Health, New Brunswick, NJ 08901, United States
| |
Collapse
|
19
|
Kim MJ, Martin CA, Kim J, Jablonski MM. Computational methods in glaucoma research: Current status and future outlook. Mol Aspects Med 2023; 94:101222. [PMID: 37925783 PMCID: PMC10842846 DOI: 10.1016/j.mam.2023.101222] [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: 08/01/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.
Collapse
Affiliation(s)
- Minjae J Kim
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Cole A Martin
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Jinhwa Kim
- Graduate School of Artificial Intelligence, Graduate School of Metaverse, Department of Management Information Systems, Sogang University, 1 Shinsoo-Dong, Mapo-Gu, Seoul, South Korea.
| | - Monica M Jablonski
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| |
Collapse
|
20
|
He X, Li SM. Gene-environment interaction in myopia. Ophthalmic Physiol Opt 2023; 43:1438-1448. [PMID: 37486033 DOI: 10.1111/opo.13206] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 07/25/2023]
Abstract
Myopia is a health issue that has attracted global attention due to its high prevalence and vision-threatening complications. It is well known that the onset and progression of myopia are related to both genetic and environmental factors: more than 450 common genetic loci have been found to be associated with myopia, while near work and outdoor time are the main environmental risk factors. As for many complex traits, gene-environment interactions are implicated in myopia development. To date, several genetic loci have been found to interact with near work or educational level. Gene-environment interaction research on myopia could yield models that provide more accurate risk predictions, thus improving targeted treatments and preventive strategies. Additionally, such investigations might have the potential to reveal novel genetic information. In this review, we summarised the findings in this field and proposed some topics for future investigations.
Collapse
Affiliation(s)
- Xi He
- Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China
| | - Shi-Ming Li
- Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China
| |
Collapse
|
21
|
Kang CC, Lee TY, Lim WF, Yeo WWY. Opportunities and challenges of 5G network technology toward precision medicine. Clin Transl Sci 2023; 16:2078-2094. [PMID: 37702288 PMCID: PMC10651640 DOI: 10.1111/cts.13640] [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: 04/28/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.
Collapse
Affiliation(s)
- Chia Chao Kang
- School of Electrical Engineering and Artificial IntelligenceXiamen University MalaysiaSepangSelangorMalaysia
| | - Tze Yan Lee
- School of Liberal Arts, Science and Technology (PUScLST)Perdana UniversityKuala LumpurMalaysia
| | - Wai Feng Lim
- Sunway Medical CentreSubang JayaSelangor Darul EhsanMalaysia
| | - Wendy Wai Yeng Yeo
- School of PharmacyMonash University MalaysiaBandar SunwaySelangor Darul EhsanMalaysia
| |
Collapse
|
22
|
Mhatre I, Abdelhalim H, Degroat W, Ashok S, Liang BT, Ahmed Z. Functional mutation, splice, distribution, and divergence analysis of impactful genes associated with heart failure and other cardiovascular diseases. Sci Rep 2023; 13:16769. [PMID: 37798313 PMCID: PMC10556087 DOI: 10.1038/s41598-023-44127-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 10/04/2023] [Indexed: 10/07/2023] Open
Abstract
Cardiovascular disease (CVD) is caused by a multitude of complex and largely heritable conditions. Identifying key genes and understanding their susceptibility to CVD in the human genome can assist in early diagnosis and personalized treatment of the relevant patients. Heart failure (HF) is among those CVD phenotypes that has a high rate of mortality. In this study, we investigated genes primarily associated with HF and other CVDs. Achieving the goals of this study, we built a cohort of thirty-five consented patients, and sequenced their serum-based samples. We have generated and processed whole genome sequence (WGS) data, and performed functional mutation, splice, variant distribution, and divergence analysis to understand the relationships between each mutation type and its impact. Our variant and prevalence analysis found FLNA, CST3, LGALS3, and HBA1 linked to many enrichment pathways. Functional mutation analysis uncovered ACE, MME, LGALS3, NR3C2, PIK3C2A, CALD1, TEK, and TRPV1 to be notable and potentially significant genes. We discovered intron, 5' Flank, 3' UTR, and 3' Flank mutations to be the most common among HF and other CVD genes. Missense mutations were less common among HF and other CVD genes but had more of a functional impact. We reported HBA1, FADD, NPPC, ADRB2, ADBR1, MYH6, and PLN to be consequential based on our divergence analysis.
Collapse
Affiliation(s)
- Ishani Mhatre
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Habiba Abdelhalim
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - William Degroat
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Shreya Ashok
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA
| | - Bruce T Liang
- Pat and Jim Calhoun Cardiology Center, UConn Health, 263 Farmington Ave, Farmington, CT, USA
- UConn School of Medicine, University of Connecticut, 263 Farmington Ave, Farmington, CT, USA
| | - Zeeshan Ahmed
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ, 08901, USA.
- Department of Genetics and Genome Sciences, UConn Health, 400 Farmington Ave, Farmington, CT, USA.
- Department of Medicine/Cardiovascular Disease and Hypertension, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
| |
Collapse
|
23
|
Peng Q, Ren B, Xin K, Liu W, Alam MS, Yang Y, Gu X, Zhu Y, Tian Y. CYFIP2 serves as a prognostic biomarker and correlates with tumor immune microenvironment in human cancers. Eur J Med Res 2023; 28:364. [PMID: 37735711 PMCID: PMC10515071 DOI: 10.1186/s40001-023-01366-2] [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: 07/11/2023] [Accepted: 09/12/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND The mechanisms whereby CYFIP2 acts in tumor development and drives immune infiltration have been poorly explored. Thus, this study aimed to identifying the role of CYFIP2 in tumors and immune response. METHODS In this study, we first explored expression patterns, diagnostic role and prognostic value of CYFIP2 in cancers, particularly in lung adenocarcinoma (LUAD). Then, we performed functional enrichment, genetic alterations, DNA methylation analysis, and immune cell infiltration analysis of CYFIP2 to uncover its potential mechanisms involved in immune microenvironment. RESULTS We found that CYFIP2 significantly differentially expressed in different tumors including LUAD compared with normal tissues. Furthermore, CYFIP2 was found to be significantly correlated with clinical parameters in LUAD. According to the diagnostic and survival analysis, CYFIP2 may be employed as a potential diagnostic and prognostic biomarker. Moreover, genetic alterations revealed that mutation of CYFIP2 was the main types of alterations in different cancers. DNA methylation analysis indicated that CYFIP2 mRNA expression correlated with hypomethylation. Afterwards, functional enrichment analysis uncovered that CYFIP2 was involved in tumor-associated and immune-related pathways. Immune infiltration analysis indicated that CYFIP2 was significantly correlated with immune cells infiltration. In particular, CYFIP2 was strongly linked with immune microenvironment scores. Additionally, CYFIP2 exhibited a significant relationship with immune regulators and immune-related genes including chemokines, chemokines receptors, and MHC genes. CONCLUSION Our results suggested that CYFIP2 may serve as a prognostic cancer biomarker for determining prognosis and might be a promising therapeutic strategy for tumor immunotherapy.
Collapse
Affiliation(s)
- Qiliang Peng
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
| | - Bixin Ren
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Kedao Xin
- Department of Radiation Oncology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China
| | - Weihui Liu
- Department of Oncology, Dazhou Central Hospital, Dazhou, China
| | - Md Shahin Alam
- Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Yinyin Yang
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Xuhao Gu
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Yaqun Zhu
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China.
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China.
| | - Ye Tian
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China.
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
| |
Collapse
|
24
|
Yap JYY, Goh LSH, Lim AJW, Chong SS, Lim LJ, Lee CG. Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:3749. [PMID: 37509410 PMCID: PMC10377993 DOI: 10.3390/cancers15143749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 07/30/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.
Collapse
Affiliation(s)
- Josephine Yu Yan Yap
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- NUS Graduate School, National University of Singapore, Singapore 119077, Singapore
| | - Laura Shih Hui Goh
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Ashley Jun Wei Lim
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Samuel S Chong
- Department of Paediatrics and Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore
| | - Lee Jin Lim
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Caroline G Lee
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- NUS Graduate School, National University of Singapore, Singapore 119077, Singapore
- Division of Cellular & Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore, Singapore 168583, Singapore
- Duke-NUS Medical School, Singapore 169857, Singapore
| |
Collapse
|
25
|
Patel KK, Venkatesan C, Abdelhalim H, Zeeshan S, Arima Y, Linna-Kuosmanen S, Ahmed Z. Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility. Hum Genomics 2023; 17:47. [PMID: 37270590 DOI: 10.1186/s40246-023-00498-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 05/31/2023] [Indexed: 06/05/2023] Open
Abstract
Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.
Collapse
Affiliation(s)
- Kush Ketan Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Cynthia Venkatesan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Yuichiro Arima
- Developmental Cardiology Laboratory, International Research Center for Medical Sciences, Kumamoto University, 2-2-1 Honjo, Kumamoto City, Kumamoto, Japan
| | - Suvi Linna-Kuosmanen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211, Kuopio, Finland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Zeeshan Ahmed
- Department of Genetics and Genome Sciences, UConn Health, 400 Farmington Ave, Farmington, CT, USA.
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
| |
Collapse
|
26
|
Long H, Li S, Chen Y. Digital health in chronic obstructive pulmonary disease. Chronic Dis Transl Med 2023; 9:90-103. [PMID: 37305103 PMCID: PMC10249197 DOI: 10.1002/cdt3.68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/11/2023] [Accepted: 04/03/2023] [Indexed: 06/13/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.
Collapse
Affiliation(s)
- Huanyu Long
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
| | - Shurun Li
- Peking University Health Science CenterBeijingChina
| | - Yahong Chen
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
| |
Collapse
|
27
|
Tsakiroglou M, Evans A, Pirmohamed M. Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis. Front Genet 2023; 14:1100352. [PMID: 36968610 PMCID: PMC10036914 DOI: 10.3389/fgene.2023.1100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.
Collapse
Affiliation(s)
- Maria Tsakiroglou
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Maria Tsakiroglou,
| | - Anthony Evans
- Computational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| |
Collapse
|
28
|
Venkat V, Abdelhalim H, DeGroat W, Zeeshan S, Ahmed Z. Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine. Genomics 2023; 115:110584. [PMID: 36813091 DOI: 10.1016/j.ygeno.2023.110584] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/06/2023] [Accepted: 02/11/2023] [Indexed: 02/22/2023]
Abstract
Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. Rightful application of artificial intelligence (AI) and machine learning (ML) approaches can lead to new insights into CVDs for providing better personalized treatments with predictive analysis and deep phenotyping. In this study we focused on implementing AI/ML techniques on RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. The study involved generating RNA-seq data derived from the serum of consented CVD patients. Next, we processed the sequenced data using our RNA-seq pipeline and applied GVViZ for gene-disease data annotation and expression analysis. To achieve our research objectives, we developed a new Findable, Accessible, Intelligent, and Reproducible (FAIR) approach that includes a five-level biostatistical evaluation, primarily based on the Random Forest (RF) algorithm. During our AI/ML analysis, we have fitted, trained, and implemented our model to classify and distinguish high-risk CVD patients based on their age, gender, and race. With the successful execution of our model, we predicted the association of highly significant HF, AF, and other CVDs genes with demographic variables.
Collapse
Affiliation(s)
- Vignesh Venkat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - William DeGroat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA; Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
| |
Collapse
|
29
|
Ahmed Z, Zeeshan S, Lee D. Editorial: Artificial intelligence for personalized and predictive genomics data analysis. Front Genet 2023; 14:1162869. [PMID: 36936434 PMCID: PMC10020608 DOI: 10.3389/fgene.2023.1162869] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Affiliation(s)
- Zeeshan Ahmed
- Rutgers Institute for Health, Healthcare Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, NJ, United States
- *Correspondence: Zeeshan Ahmed,
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Donghyung Lee
- Department of Statistics, Miami University, Oxford, OH, United States
| |
Collapse
|
30
|
Fan Y, S Chan A, Zhu J, Yi Leung S, Fan X. A Bayesian model for identifying cancer subtypes from paired methylation profiles. Brief Bioinform 2022; 24:6961790. [PMID: 36575828 PMCID: PMC9851340 DOI: 10.1093/bib/bbac568] [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: 07/29/2022] [Revised: 10/19/2022] [Accepted: 11/22/2022] [Indexed: 12/29/2022] Open
Abstract
Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.
Collapse
Affiliation(s)
- Yetian Fan
- School of Mathematics and Statistics, Liaoning University, Shenyang, 110036, China,Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong SAR, China
| | - April S Chan
- Department of Pathology, School of Clinical Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jun Zhu
- Sema4, Stamford, CT, 06902, USA,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suet Yi Leung
- Department of Pathology, School of Clinical Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xiaodan Fan
- Corresponding author: Xiaodan Fan, Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong SAR, China. E-mail:
| |
Collapse
|
31
|
Abdelhalim H, Berber A, Lodi M, Jain R, Nair A, Pappu A, Patel K, Venkat V, Venkatesan C, Wable R, Dinatale M, Fu A, Iyer V, Kalove I, Kleyman M, Koutsoutis J, Menna D, Paliwal M, Patel N, Patel T, Rafique Z, Samadi R, Varadhan R, Bolla S, Vadapalli S, Ahmed Z. Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine. Front Genet 2022; 13:929736. [PMID: 35873469 PMCID: PMC9299079 DOI: 10.3389/fgene.2022.929736] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/25/2022] [Indexed: 12/13/2022] Open
Abstract
Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.
Collapse
Affiliation(s)
- Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Asude Berber
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mudassir Lodi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rihi Jain
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Achuth Nair
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Anirudh Pappu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Kush Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vignesh Venkat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Cynthia Venkatesan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Raghu Wable
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Matthew Dinatale
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Allyson Fu
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Vikram Iyer
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Ishan Kalove
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Marc Kleyman
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Joseph Koutsoutis
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - David Menna
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Mayank Paliwal
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Nishi Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Thirth Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zara Rafique
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Rothela Samadi
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Roshan Varadhan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Shreyas Bolla
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United States.,Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, NJ, United States
| |
Collapse
|
32
|
Berber A, Abdelhalim H, Zeeshan S, Vadapalli S, von Oehsen B, Yanamala N, Sengupta P, Ahmed Z. RNA-seq-driven expression analysis to investigate cardiovascular disease genes with associated phenotypes among atrial fibrillation patients. Clin Transl Med 2022; 12:e974. [PMID: 35875838 PMCID: PMC9309637 DOI: 10.1002/ctm2.974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Asude Berber
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey, USA
| | - Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA
| | - Barr von Oehsen
- Office of Advanced Research Computing, Rutgers, The State University of New Jersey, Computing Research and Education (CoRE) Building, Piscataway, New Jersey, USA
| | - Naveena Yanamala
- Division of Cardiovascular Disease, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, New Jersey, USA
| | - Partho Sengupta
- Division of Cardiovascular Disease, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, New Jersey, USA
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA.,Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, New Jersey, USA
| |
Collapse
|