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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.
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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.
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Ahuja SK, Shrimankar DD, Durge AR. A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review. Curr Genomics 2023; 24:207-235. [PMID: 38169652 PMCID: PMC10758128 DOI: 10.2174/0113892029269523231101051455] [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: 06/28/2023] [Revised: 10/05/2023] [Accepted: 10/05/2023] [Indexed: 01/05/2024] Open
Abstract
Human gene sequences are considered a primary source of comprehensive information about different body conditions. A wide variety of diseases including cancer, heart issues, brain issues, genetic issues, etc. can be pre-empted via efficient analysis of genomic sequences. Researchers have proposed different configurations of machine learning models for processing genomic sequences, and each of these models varies in terms of their performance & applicability characteristics. Models that use bioinspired optimizations are generally slower, but have superior incremental-performance, while models that use one-shot learning achieve higher instantaneous accuracy but cannot be scaled for larger disease-sets. Due to such variations, it is difficult for genomic system designers to identify optimum models for their application-specific & performance-specific use cases. To overcome this issue, a detailed survey of different genomic processing models in terms of their functional nuances, application-specific advantages, deployment-specific limitations, and contextual future scopes is discussed in this text. Based on this discussion, researchers will be able to identify optimal models for their functional use cases. This text also compares the reviewed models in terms of their quantitative parameter sets, which include, the accuracy of classification, delay needed to classify large-length sequences, precision levels, scalability levels, and deployment cost, which will assist readers in selecting deployment-specific models for their contextual clinical scenarios. This text also evaluates a novel Genome Processing Efficiency Rank (GPER) for each of these models, which will allow readers to identify models with higher performance and low overheads under real-time scenarios.
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Affiliation(s)
- Sony K. Ahuja
- Visvesvaraya National Institute of Technology, Computer Science and Engineering, India
| | - Deepti D. Shrimankar
- Visvesvaraya National Institute of Technology, Computer Science and Engineering, India
| | - Aditi R. Durge
- Visvesvaraya National Institute of Technology, Computer Science and Engineering, India
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Cook S. Editorial: Debates in coronary artery disease: 2022. Front Cardiovasc Med 2023; 10:1287772. [PMID: 37829694 PMCID: PMC10565514 DOI: 10.3389/fcvm.2023.1287772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 09/11/2023] [Indexed: 10/14/2023] Open
Affiliation(s)
- Stéphane Cook
- Coronary Artery Disease, Frontiers in Cardiovascular Medicine, Head of Cardiology, University & Hospital, Fribourg, Switzerland
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Doh CY, Kampourakis T, Campbell KS, Stelzer JE. Basic science methods for the characterization of variants of uncertain significance in hypertrophic cardiomyopathy. Front Cardiovasc Med 2023; 10:1238515. [PMID: 37600050 PMCID: PMC10432852 DOI: 10.3389/fcvm.2023.1238515] [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: 06/11/2023] [Accepted: 07/20/2023] [Indexed: 08/22/2023] Open
Abstract
With the advent of next-generation whole genome sequencing, many variants of uncertain significance (VUS) have been identified in individuals suffering from inheritable hypertrophic cardiomyopathy (HCM). Unfortunately, this classification of a genetic variant results in ambiguity in interpretation, risk stratification, and clinical practice. Here, we aim to review some basic science methods to gain a more accurate characterization of VUS in HCM. Currently, many genomic data-based computational methods have been developed and validated against each other to provide a robust set of resources for researchers. With the continual improvement in computing speed and accuracy, in silico molecular dynamic simulations can also be applied in mutational studies and provide valuable mechanistic insights. In addition, high throughput in vitro screening can provide more biologically meaningful insights into the structural and functional effects of VUS. Lastly, multi-level mathematical modeling can predict how the mutations could cause clinically significant organ-level dysfunction. We discuss emerging technologies that will aid in better VUS characterization and offer a possible basic science workflow for exploring the pathogenicity of VUS in HCM. Although the focus of this mini review was on HCM, these basic science methods can be applied to research in dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic cardiomyopathy (ACM), or other genetic cardiomyopathies.
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Affiliation(s)
- Chang Yoon Doh
- School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Thomas Kampourakis
- Randall Centre for Cell and Molecular Biophysics, and British Heart Foundation Centre of Research Excellence, King’s College London, London, United Kingdom
| | - Kenneth S. Campbell
- Division of Cardiovascular Medicine, University of Kentucky, Lexington, KY, United States
| | - Julian E. Stelzer
- Department of Physiology and Biophysics, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
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Khawaja M, Siddiqui R, Virani SS, Amos CI, Bandyopadhyay D, Virk HUH, Alam M, Jneid H, Krittanawong C. Integrative Genetic Approach Facilitates Precision Strategies for Acute Myocardial Infarction. Genes (Basel) 2023; 14:1340. [PMID: 37510245 PMCID: PMC10379681 DOI: 10.3390/genes14071340] [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: 05/22/2023] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023] Open
Abstract
Acute myocardial infarction remains a significant cause of mortality worldwide and its burden continues to grow. Its pathophysiology is known to be complex and multifactorial, with several acquired and inherited risk factors. As advances in technology and medical therapy continue, there is now increasing recognition of the role that genetics play in the development and management of myocardial infarction. The genetic determinants of acute coronary syndrome are still vastly understudied, but the advent of whole-genome scanning and genome-wide association studies has significantly expanded the current understanding of genetics and simultaneously fostered hope that genetic profiling and gene-guided treatments could substantially impact clinical outcomes. The identification of genes associated with acute myocardial infarction can help in the development of personalized medicine, risk stratification, and improved therapeutic strategies. In this context, several genes have been studied, and their potential involvement in increasing the risk for acute myocardial infarction is being investigated. As such, this article provides a review of some of the genes potentially related to an increased risk for acute myocardial infarction as well as the latest updates in gene-guided risk stratification and treatment strategies.
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Affiliation(s)
- Muzamil Khawaja
- Department of Cardiology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Rehma Siddiqui
- Department of Internal Medicine, The University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Salim S Virani
- Department of Cardiology, The Aga Khan University, Karachi 74800, Pakistan
- Department of Cardiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Christopher I Amos
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77054, USA
| | - Dhrubajyoti Bandyopadhyay
- Department of Cardiology, Westchester Medical Centre, New York Medical College, Valhalla, NY 10595, USA
| | - Hafeez Ul Hassan Virk
- Department of Cardiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Mahboob Alam
- Department of Cardiology, The Texas Heart Institute, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hani Jneid
- Department of Cardiology, University of Texas Medical Branch, Houston, TX 77030, USA
| | - Chayakrit Krittanawong
- Department of Cardiology, NYU Langone Health and NYU School of Medicine, New York, NY 10016, USA
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Kresoja KP, Unterhuber M, Wachter R, Thiele H, Lurz P. A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction. Basic Res Cardiol 2023; 118:10. [PMID: 36939941 PMCID: PMC10027799 DOI: 10.1007/s00395-023-00982-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
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Affiliation(s)
- Karl-Patrik Kresoja
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig, Leipzig, Germany
- Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany
- German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| | - Philipp Lurz
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
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Pitchai R, Dappuri B, Pramila PV, Vidhyalakshmi M, Shanthi S, Alonazi WB, Almutairi KMA, Sundaram RS, Beyene I. An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5489084. [PMID: 36275965 PMCID: PMC9581610 DOI: 10.1155/2022/5489084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/10/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022]
Abstract
Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.
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Affiliation(s)
- R. Pitchai
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur 502313, Telangana, India
| | - Bhasker Dappuri
- Department of Electronics and Communication Engineering, CMR Engineering College, Kandlakoya 501401, Telangana, India
| | - P. V. Pramila
- Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 600124, Tamil Nadu, India
| | - M. Vidhyalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai 600089, Tamilnadu, India
| | - S. Shanthi
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai 638060, Tamil Nadu, India
| | - Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, P. O Box: 71115, Riyadh 11587, Saudi Arabia
| | - Khalid M. A. Almutairi
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box 10219, Riyadh 11433, Saudi Arabia
| | - R. S. Sundaram
- Department of Health Sciences, University of Texas, TX, USA
| | - Ibsa Beyene
- Department of IT, Mettu University, Mettu, Ethiopia
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Battineni G, Hossain MA, Chintalapudi N, Amenta F. A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review. Diagnostics (Basel) 2022; 12:1179. [PMID: 35626333 PMCID: PMC9140088 DOI: 10.3390/diagnostics12051179] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 02/04/2023] Open
Abstract
Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.
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Affiliation(s)
- Gopi Battineni
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (F.A.)
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