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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 [...].
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Affiliation(s)
| | - Alina Crenguța Nicolae
- Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, 020956 Bucharest, Romania; (C.M.D.); (I.-B.D.)
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2
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Soyer SM, Ozbek P, Kasavi C. Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:408-420. [PMID: 38979602 DOI: 10.1089/omi.2024.0121] [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: 07/10/2024]
Abstract
Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.
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Affiliation(s)
- Semra Melis Soyer
- Department of Bioengineering, Faculty of Engineering, Marmara University, İstanbul, Türkiye
| | - Pemra Ozbek
- Department of Bioengineering, Faculty of Engineering, Marmara University, İstanbul, Türkiye
| | - Ceyda Kasavi
- Department of Bioengineering, Faculty of Engineering, Marmara University, İstanbul, Türkiye
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3
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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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4
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Rashid A, Al-Obeida F, Hafez W, Benakatti G, Malik RA, Koutentis C, Sharief J, Brierley J, Quraishi N, Malik ZA, Anwary A, Alkhzaimi H, Zaki SA, Khilnani P, Kadwa R, Phatak R, Schumacher M, Shaikh G, Al-Dubai A, Hussain A. ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES. Shock 2024; 61:4-18. [PMID: 37752080 DOI: 10.1097/shk.0000000000002227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
ABSTRACT Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.
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Affiliation(s)
| | | | | | | | | | | | | | - Joe Brierley
- Great Ormond Street Children's Hospital, London, UK
| | - Nasir Quraishi
- Centre for Spinal Studies & Surgery, Queen's Medical Centre. The University of Nottingham. Nottingham, UK
| | - Zainab A Malik
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences. Dubai, U.A.E
| | - Arif Anwary
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | | | | | | | | | - Rajesh Phatak
- Pediatric Intensive Care, Burjeel Hospital, Najda, Abu Dhabi
| | | | - Guftar Shaikh
- Endocrinology, Royal Hospital for Children. Glasgow, UK
| | - Ahmed Al-Dubai
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | - Amir Hussain
- School of Computing, Edinburgh Napier University. Edinburgh, UK
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5
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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).
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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
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Mumtaz H, Saqib M, Jabeen S, Muneeb M, Mughal W, Sohail H, Safdar M, Mehmood Q, Khan MA, Ismail SM. Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review. Front Med (Lausanne) 2023; 10:1227168. [PMID: 37849490 PMCID: PMC10577305 DOI: 10.3389/fmed.2023.1227168] [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: 05/22/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023] Open
Abstract
The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.
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Affiliation(s)
| | | | | | - Muhammad Muneeb
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Wajiha Mughal
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Hassan Sohail
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Myra Safdar
- Armed Forces Institute of Cardiology and National Institute of Heart Diseases (AFIC-NIHD), Rawalpindi, Pakistan
| | - Qasim Mehmood
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Muhammad Ahsan Khan
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
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Favreau E, Geist KS, Wyatt CDR, Toth AL, Sumner S, Rehan SM. Co-expression Gene Networks and Machine-learning Algorithms Unveil a Core Genetic Toolkit for Reproductive Division of Labour in Rudimentary Insect Societies. Genome Biol Evol 2023; 15:evac174. [PMID: 36527688 PMCID: PMC9830183 DOI: 10.1093/gbe/evac174] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 12/06/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022] Open
Abstract
The evolution of eusociality requires that individuals forgo some or all their own reproduction to assist the reproduction of others in their group, such as a primary egg-laying queen. A major open question is how genes and genetic pathways sculpt the evolution of eusociality, especially in rudimentary forms of sociality-those with smaller cooperative nests when compared with species such as honeybees that possess large societies. We lack comprehensive comparative studies examining shared patterns and processes across multiple social lineages. Here we examine the mechanisms of molecular convergence across two lineages of bees and wasps exhibiting such rudimentary societies. These societies consist of few individuals and their life histories range from facultative to obligately social. Using six species across four independent origins of sociality, we conduct a comparative meta-analysis of publicly available transcriptomes. Standard methods detected little similarity in patterns of differential gene expression in brain transcriptomes among reproductive and non-reproductive individuals across species. By contrast, both supervised machine learning and consensus co-expression network approaches uncovered sets of genes with conserved expression patterns among reproductive and non-reproductive phenotypes across species. These sets overlap substantially, and may comprise a shared genetic "toolkit" for sociality across the distantly related taxa of bees and wasps and independently evolved lineages of sociality. We also found many lineage-specific genes and co-expression modules associated with social phenotypes and possible signatures of shared life-history traits. These results reveal how taxon-specific molecular mechanisms complement a core toolkit of molecular processes in sculpting traits related to the evolution of eusociality.
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Affiliation(s)
- Emeline Favreau
- Department of Genetics, Environment, Evolution, University College London, London WC1E 6BT, United Kingdom
| | - Katherine S Geist
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa 50011
| | - Christopher D R Wyatt
- Department of Genetics, Environment, Evolution, University College London, London WC1E 6BT, United Kingdom
| | - Amy L Toth
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa 50011
| | - Seirian Sumner
- Department of Genetics, Environment, Evolution, University College London, London WC1E 6BT, United Kingdom
| | - Sandra M Rehan
- Department of Biology, York University, Toronto, ON M3J 1P3, Canada
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Vadapalli S, Abdelhalim H, Zeeshan S, Ahmed Z. Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Brief Bioinform 2022; 23:6590150. [PMID: 35595537 DOI: 10.1093/bib/bbac191] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/02/2022] [Accepted: 04/26/2022] [Indexed: 12/16/2022] Open
Abstract
Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.
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Affiliation(s)
- Sreya Vadapalli
- 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
| | - 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
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Augustine J, Jereesh AS. Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson's disease using two-layer hybrid feature selection. Gene X 2022; 823:146366. [PMID: 35202733 DOI: 10.1016/j.gene.2022.146366] [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: 09/04/2021] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 11/19/2022] Open
Abstract
Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.
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Affiliation(s)
- Jisha Augustine
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
| | - A S Jereesh
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
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