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Rashidi HH, Ikram A, Dang LT, Bashir A, Zohra T, Ali A, Tanvir H, Mudassar M, Ravindran R, Akhtar N, Sikandar RI, Umer M, Akhter N, Butt R, Fennell BD, Khan IH. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings. Sci Rep 2024; 14:14892. [PMID: 38937503 PMCID: PMC11211475 DOI: 10.1038/s41598-024-63707-3] [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: 02/01/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.
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Affiliation(s)
- Hooman H Rashidi
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh Medical Center, and University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Aamer Ikram
- National Institutes of Health, Islamabad, Pakistan
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Adnan Bashir
- Health Information Systems Program (HISP), Islamabad, Pakistan
| | | | - Amna Ali
- National Institutes of Health, Islamabad, Pakistan
| | - Hamza Tanvir
- National Institutes of Health, Islamabad, Pakistan
| | | | - Resmi Ravindran
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Nasim Akhtar
- Pakistan Institute of Medical Sciences, Islamabad, Pakistan
| | | | - Mohammed Umer
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Naeem Akhter
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Rafi Butt
- Isolation Hospital and Infectious Treatment Centre, Islamabad, Pakistan
| | - Brandon D Fennell
- Department of Medicine, University of California, San Francisco, USA
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA.
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Ali M, Huarte OU, Heurtaux T, Garcia P, Rodriguez BP, Grzyb K, Halder R, Skupin A, Buttini M, Glaab E. Single-Cell Transcriptional Profiling and Gene Regulatory Network Modeling in Tg2576 Mice Reveal Gender-Dependent Molecular Features Preceding Alzheimer-Like Pathologies. Mol Neurobiol 2024; 61:541-566. [PMID: 35980567 PMCID: PMC10861719 DOI: 10.1007/s12035-022-02985-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 07/29/2022] [Indexed: 11/26/2022]
Abstract
Alzheimer's disease (AD) onset and progression is influenced by a complex interplay of several environmental and genetic factors, one of them gender. Pronounced gender differences have been observed both in the relative risk of developing AD and in clinical disease manifestations. A molecular level understanding of these gender disparities is still missing, but could provide important clues on cellular mechanisms modulating the disease and reveal new targets for gender-oriented disease-modifying precision therapies. We therefore present here a comprehensive single-cell analysis of disease-associated molecular gender differences in transcriptomics data from the neocortex, one of the brain regions most susceptible to AD, in one of the most widely used AD mouse models, the Tg2576 model. Cortical areas are also most commonly used in studies of post-mortem AD brains. To identify disease-linked molecular processes that occur before the onset of detectable neuropathology, we focused our analyses on an age with no detectable plaques and microgliosis. Cell-type specific alterations were investigated at the level of individual genes, pathways, and gene regulatory networks. The number of differentially expressed genes (DEGs) was not large enough to build context-specific gene regulatory networks for each individual cell type, and thus, we focused on the study of cell types with dominant changes and included analyses of changes across the combination of cell types. We observed significant disease-associated gender differences in cellular processes related to synapse organization and reactive oxygen species metabolism, and identified a limited set of transcription factors, including Egr1 and Klf6, as key regulators of many of the disease-associated and gender-dependent gene expression changes in the model. Overall, our analyses revealed significant cell-type specific gene expression changes in individual genes, pathways and sub-networks, including gender-specific and gender-dimorphic changes in both upstream transcription factors and their downstream targets, in the Tg2576 AD model before the onset of overt disease. This opens a window into molecular events that could determine gender-susceptibility to AD, and uncovers tractable target candidates for potential gender-specific precision medicine for AD.
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Affiliation(s)
- Muhammad Ali
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
- School for Mental Health and Neuroscience (MHeNs), Department of Psychiatry and Neuropsychology, Maastricht University, 6200, Maastricht, the Netherlands
| | - Oihane Uriarte Huarte
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), L-3555, Dudelange, Luxembourg
| | - Tony Heurtaux
- Luxembourg Center of Neuropathology (LCNP), L-3555, Dudelange, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, L‑4362, Esch-Sur-Alzette, Luxembourg
| | - Pierre Garcia
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), L-3555, Dudelange, Luxembourg
| | - Beatriz Pardo Rodriguez
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), L-3555, Dudelange, Luxembourg
- University of the Basque Country, Cell Biology and Histology Department, 48940, Leioa, Vizcaya, Basque Country, Spain
| | - Kamil Grzyb
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
- Department of Physics and Materials Science, University of Luxembourg, 162a av. de la Faïencerie, 1511, Luxembourg, Luxembourg
- Department of Neuroscience, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Manuel Buttini
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), L-3555, Dudelange, Luxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.
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3
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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Singh N, Bhatnagar S. Machine Learning for Prediction of Drug Targets in Microbe Associated Cardiovascular Diseases by Incorporating Host-pathogen Interaction Network Parameters. Mol Inform 2021; 41:e2100115. [PMID: 34676983 DOI: 10.1002/minf.202100115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 10/01/2021] [Indexed: 12/20/2022]
Abstract
Host-pathogen interactions play a crucial role in invasion, infection, and induction of immune response in humans. In this work, four machine learning algorithms, namely Logistic regression, K-nearest neighbor, Support Vector Machine, and Random Forest were implemented for the classification of drug targets. The algorithms were trained using 3400 hosts and 3800 pathogen drug and non-drug target proteins as learning instances. For each protein, 68 pathogen and 73 host features were computed that included sequence, structure, biological and host-pathogen network centrality characteristics. The Random Forest classifier model achieved the best accuracy after 10-fold cross-validation. 99 % accuracy was achieved with a ROC-AUC score of 0.99±0.01 for both pathogen and host training sets. The Eigenvector Centrality of host-pathogen interactions and host-host interactions was the top feature in performing classification of pathogen and host targets respectively. Other features important for classification were the presence of catalytic and binding sites, low instability/aliphatic index, and cellular location. The Random Forest classifier was then used for prediction of drug targets involved in Microbe Associated Cardiovascular Diseases. 331 host and 743 pathogen proteins were predicted as drug targets by the random forest model and can be validated experimentally for therapeutic intervention in Microbe Associated Cardiovascular Diseases.
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Affiliation(s)
- Nirupma Singh
- Department of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India
| | - Sonika Bhatnagar
- Department of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, 110078, India.,Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology Dwarka, New Delhi, 110078, India
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Rashidi HH, Dang LT, Albahra S, Ravindran R, Khan IH. Automated machine learning for endemic active tuberculosis prediction from multiplex serological data. Sci Rep 2021; 11:17900. [PMID: 34504228 PMCID: PMC8429671 DOI: 10.1038/s41598-021-97453-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 08/25/2021] [Indexed: 11/09/2022] Open
Abstract
Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addressed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). MILO seamlessly integrates data processing, feature selection, model training, and model validation to simultaneously generate and evaluate thousands of models. These models were then further tested for generalizability on out-of-sample secondary and tertiary datasets. Out of 31 antigens evaluated, a 23-antigen model was the most robust on both the secondary dataset (TB vs healthy) and the tertiary dataset (TB vs COPD) with sensitivity of 90.5% and respective specificities of 100.0% and 74.6%. MILO represents a user-friendly, end-to-end solution for automated generation and deployment of optimized models, ideal for applications where rapid clinical implementation is critical such as emerging infectious diseases.
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Affiliation(s)
- Hooman H Rashidi
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA.
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA
| | - Samer Albahra
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA
| | - Resmi Ravindran
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA.
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Dashwood M, Churchhouse G, Young M, Kuruvilla T. Artificial intelligence as an aid to diagnosing dementia: an overview. PROGRESS IN NEUROLOGY AND PSYCHIATRY 2021. [DOI: 10.1002/pnp.721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Mark Dashwood
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
| | - Gabrielle Churchhouse
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
| | - Matilda Young
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
| | - Tarun Kuruvilla
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
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Das B, Mitra P. Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins. J Mol Biol 2021; 433:167149. [PMID: 34271012 DOI: 10.1016/j.jmb.2021.167149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/11/2021] [Accepted: 07/06/2021] [Indexed: 10/20/2022]
Abstract
Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of 83.45% while AUROC and AUPRC values are 0.90 and 0.88, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.
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Affiliation(s)
- Barnali Das
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur 721302, India.
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease. Aging Dis 2020; 11:1567-1584. [PMID: 33269107 PMCID: PMC7673858 DOI: 10.14336/ad.2020.0312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.
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Affiliation(s)
- Rohan Mishra
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
| | - Bin Li
- Washington Institute for Health Sciences, Arlington, VA 22203, USA
- Georgetown University Medical Center, Washington D.C. 20057, USA
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Abstract
Signal transduction tasks as well as other complex biological processes involve many different changes in groups of genes, proteins, and metabolites linked together in chains or networks called pathways or networks of pathways. In a classical functional analysis, the biomolecules found to play a role in the biological status under investigation are members of a group of pathways that are not necessarily interconnected. However, interconnectivity is a critical factor for functionality. Thus, it is necessary to be able to construct "connected functional stories" to understand better the complex biological processes. PathwayConnector is a recently introduced web-tool that facilitates the construction of complementary pathway-to-pathway networks, bringing to our attention missing pathways that are crucial links towards the understanding of the molecular mechanisms related to complex diseases. Current version of the web-tool draws from an expanded pathway reference network and provides information deriving from 19 different organisms and 2 different pathway repositories: the KEGG and the REACTOME. Novel genes, proteins, and pathways derived from any experimental/computational method either in large-scale (omics) or even in smaller scale (specific laboratory experiments) can potentially be projected and analyzed through PathwayConnector. This chapter describes in details the pipeline and methodologies used for the latest updated version of PathwayConnector, providing an easy way for rapidly relating human or other organism's pathways together. Recent studies have shown that pathway networks and subnetworks, generated by PathwayConnector, are an integral part towards the individualization of disease, leading to a more precise and personalized management of the treatment.
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Affiliation(s)
- George Minadakis
- Department of Bioinformatics, The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus.
| | - George M Spyrou
- Department of Bioinformatics, The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
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13
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Jamal S, Khubaib M, Gangwar R, Grover S, Grover A, Hasnain SE. Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis. Sci Rep 2020; 10:5487. [PMID: 32218465 PMCID: PMC7099008 DOI: 10.1038/s41598-020-62368-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 03/13/2020] [Indexed: 11/09/2022] Open
Abstract
Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods generate a large amount of data which could be exploited in determining multi-drug resistant (MDR-TB) associated mutations. The present work is a computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in the genes rpoB, inhA, katG, pncA, gyrA and gyrB for the drugs rifampicin, isoniazid, pyrazinamide and fluoroquinolones. The single nucleotide variations were represented by several sequence and structural features that indicate the influence of mutations on the target protein coded by each gene. We used ML algorithms - naïve bayes, k nearest neighbor, support vector machine, and artificial neural network, to build the prediction models. The classification models had an average accuracy of 85% across all examined genes and were evaluated on an external unseen dataset to demonstrate their application. Further, molecular docking and molecular dynamics simulations were performed for wild type and predicted resistance causing mutant protein and anti-TB drug complexes to study their impact on the conformation of proteins to confirm the observed phenotype.
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Affiliation(s)
- Salma Jamal
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Mohd Khubaib
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Rishabh Gangwar
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Sonam Grover
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Mehrauli Road, New Delhi, 110 067, India
| | - Seyed E Hasnain
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India. .,Dr. Reddy's Institute of Life Sciences, University of Hyderabad Campus, Professor C.R. Rao Road, Hyderabad, 500046, India.
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Oral lichen planus interactome reveals CXCR4 and CXCL12 as candidate therapeutic targets. Sci Rep 2020; 10:5454. [PMID: 32214134 PMCID: PMC7096434 DOI: 10.1038/s41598-020-62258-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Accepted: 03/12/2020] [Indexed: 01/03/2023] Open
Abstract
Today, we face difficulty in generating new hypotheses and understanding oral lichen planus due to the large amount of biomedical information available. In this research, we have used an integrated bioinformatics approach assimilating information from data mining, gene ontologies, protein–protein interaction and network analysis to predict candidate genes related to oral lichen planus. A detailed pathway analysis led us to propose two promising therapeutic targets: the stromal cell derived factor 1 (CXCL12) and the C-X-C type 4 chemokine receptor (CXCR4). We further validated our predictions and found that CXCR4 was upregulated in all oral lichen planus tissue samples. Our bioinformatics data cumulatively support the pathological role of chemokines and chemokine receptors in oral lichen planus. From a clinical perspective, we suggest a drug (plerixafor) and two therapeutic targets for future research.
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Graham SA, Lee EE, Jeste DV, Van Patten R, Twamley EW, Nebeker C, Yamada Y, Kim HC, Depp CA. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. Psychiatry Res 2020; 284:112732. [PMID: 31978628 PMCID: PMC7081667 DOI: 10.1016/j.psychres.2019.112732] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 12/04/2019] [Accepted: 12/07/2019] [Indexed: 12/13/2022]
Abstract
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.
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Affiliation(s)
- Sarah A Graham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States
| | - Ellen E Lee
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States
| | - Dilip V Jeste
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Department of Neurosciences, University of California San Diego, La Jolla, CA, United States.
| | - Ryan Van Patten
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States
| | - Elizabeth W Twamley
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States
| | - Camille Nebeker
- Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, United States
| | | | - Ho-Cheol Kim
- IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Scalable Knowledge Intelligence, IBM Research-Almaden, San Jose, CA, United States
| | - Colin A Depp
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States
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16
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Barman RK, Mukhopadhyay A, Maulik U, Das S. Identification of infectious disease-associated host genes using machine learning techniques. BMC Bioinformatics 2019; 20:736. [PMID: 31881961 PMCID: PMC6935192 DOI: 10.1186/s12859-019-3317-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 12/16/2019] [Indexed: 02/06/2023] Open
Abstract
Background With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets. Results We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases. Conclusions To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.
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Affiliation(s)
- Ranjan Kumar Barman
- Biomedical Informatics Centre, ICMR-National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India.,Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Santasabuj Das
- Biomedical Informatics Centre, ICMR-National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India. .,Division of Clinical Medicine, ICMR-National Institute of Cholera and Enteric Diseases, P-33, C.I.T.Road Scheme XM, Beliaghata-700010, Kolkata, West Bengal, India.
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17
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Jamal S, Ali W, Nagpal P, Grover S, Grover A. Computational models for the prediction of adverse cardiovascular drug reactions. J Transl Med 2019; 17:171. [PMID: 31118067 PMCID: PMC6530172 DOI: 10.1186/s12967-019-1918-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 05/10/2019] [Indexed: 02/06/2023] Open
Abstract
Background Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. Methods In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. Results This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. Conclusions The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development. Electronic supplementary material The online version of this article (10.1186/s12967-019-1918-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Salma Jamal
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India
| | - Waseem Ali
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India
| | - Priya Nagpal
- Department of Biotechnology, Jamia Millia Islamia, New Delhi, India
| | - Sonam Grover
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India.
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.
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18
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Oulas A, Minadakis G, Zachariou M, Sokratous K, Bourdakou MM, Spyrou GM. Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform 2019; 20:806-824. [PMID: 29186305 PMCID: PMC6585387 DOI: 10.1093/bib/bbx151] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/17/2017] [Indexed: 02/01/2023] Open
Abstract
Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George Minadakis
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kleitos Sokratous
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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Pournoor E, Elmi N, Masoudi-Nejad A. CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks. Curr Genomics 2019; 20:69-75. [PMID: 31015793 PMCID: PMC6446483 DOI: 10.2174/1389202919666181213101540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 10/19/2018] [Accepted: 12/02/2018] [Indexed: 12/17/2022] Open
Abstract
Background Complexity and dynamicity of biological events is a reason to use comprehen-sive and holistic approaches to deal with their difficulty. Currently with advances in omics data genera-tion, network-based approaches are used frequently in different areas of computational biology and bio-informatics to solve problems in a systematic way. Also, there are many applications and tools for net-work data analysis and manipulation which their goal is to facilitate the way of improving our under-standings of inter/intra cellular interactions. Methods In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Naser Elmi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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20
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Podder A, Pandit M, Narayanan L. Drug Target Prioritization for Alzheimer's Disease Using Protein Interaction Network Analysis. ACTA ACUST UNITED AC 2018; 22:665-677. [DOI: 10.1089/omi.2018.0131] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Avijit Podder
- Bioinformatics Infrastructure Facility, Sri Venkateswara College (University of Delhi), Delhi, India
| | - Mansi Pandit
- Bioinformatics Infrastructure Facility, Sri Venkateswara College (University of Delhi), Delhi, India
| | - Latha Narayanan
- Bioinformatics Infrastructure Facility, Sri Venkateswara College (University of Delhi), Delhi, India
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Dragomir A, Vrahatis AG, Bezerianos A. A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework. IEEE J Biomed Health Inform 2018; 23:14-25. [PMID: 30080151 DOI: 10.1109/jbhi.2018.2863202] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.
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22
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Mowry EM, Hedström AK, Gianfrancesco MA, Shao X, Schaefer CA, Shen L, Bellesis KH, Briggs FBS, Olsson T, Alfredsson L, Barcellos LF. Incorporating machine learning approaches to assess putative environmental risk factors for multiple sclerosis. Mult Scler Relat Disord 2018; 24:135-141. [PMID: 30005356 DOI: 10.1016/j.msard.2018.06.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 05/07/2018] [Accepted: 06/15/2018] [Indexed: 12/24/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) incidence has increased recently, particularly in women, suggesting a possible role of one or more environmental exposures in MS risk. The study objective was to determine if animal, dietary, recreational, or occupational exposures are associated with MS risk. METHODS Least absolute shrinkage and selection operator (LASSO) regression was used to identify a subset of exposures with potential relevance to disease in a large population-based (Kaiser Permanente Northern California [KPNC]) case-control study. Variables with non-zero coefficients were analyzed in matched conditional logistic regression analyses, adjusted for established environmental risk factors and socioeconomic status (if relevant in univariate screening),± genetic risk factors, in the KPNC cohort and, for purposes of replication, separately in the Swedish Epidemiological Investigation of MS cohort. These variables were also assessed in models stratified by HLA-DRB1*15:01 status since interactions between risk factors and that haplotype have been described. RESULTS There was a suggestive association of pesticide exposure with having MS among men, but only in those who were positive for HLA-DRB1*15:01 (OR pooled = 3.11, 95% CI 0.87, 11.16, p = 0.08). CONCLUSIONS While this finding requires confirmation, it is interesting given the association between pesticide exposure and other neurological diseases. The study also demonstrates the application of LASSO to identify environmental exposures with reduced multiple statistical testing penalty. Machine learning approaches may be useful for future investigations of concomitant MS risk or prognostic factors.
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Affiliation(s)
- Ellen M Mowry
- Johns Hopkins University, 600N. Wolfe Street, Pathology 627, Baltimore 21287, MD, USA.
| | - Anna K Hedström
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | - Ling Shen
- Kaiser Permanente Division of Research, Oakland, CA, USA
| | | | | | - Tomas Olsson
- Karolinska Institutet at Karolinska University Hospital, Solna, Sweden
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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Pandey B, Grover S, Goyal S, Kumari A, Singh A, Jamal S, Kaur J, Grover A. Alanine mutation of the catalytic sites of Pantothenate Synthetase causes distinct conformational changes in the ATP binding region. Sci Rep 2018; 8:903. [PMID: 29343701 PMCID: PMC5772511 DOI: 10.1038/s41598-017-19075-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 12/19/2017] [Indexed: 02/01/2023] Open
Abstract
The enzyme Pantothenate synthetase (PS) represents a potential drug target in Mycobacterium tuberculosis. Its X-ray crystallographic structure has demonstrated the significance and importance of conserved active site residues including His44, His47, Asn69, Gln72, Lys160 and Gln164 in substrate binding and formation of pantoyl adenylate intermediate. In the current study, molecular mechanism of decreased affinity of the enzyme for ATP caused by alanine mutations was investigated using molecular dynamics (MD) simulations and free energy calculations. A total of seven systems including wild-type + ATP, H44A + ATP, H47A + ATP, N69A + ATP, Q72A + ATP, K160A + ATP and Q164A + ATP were subjected to 50 ns MD simulations. Docking score, MM-GBSA and interaction profile analysis showed weak interactions between ATP (substrate) and PS (enzyme) in H47A and H160A mutants as compared to wild-type, leading to reduced protein catalytic activity. However, principal component analysis (PCA) and free energy landscape (FEL) analysis revealed that ATP was strongly bound to the catalytic core of the wild-type, limiting its movement to form a stable complex as compared to mutants. The study will give insight about ATP binding to the PS at the atomic level and will facilitate in designing of non-reactive analogue of pantoyl adenylate which will act as a specific inhibitor for PS.
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Affiliation(s)
- Bharati Pandey
- Department of Biotechnology, Panjab University, Chandigarh, 160014, India
| | - Sonam Grover
- Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Sukriti Goyal
- Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India
| | - Anchala Kumari
- Department of Biotechnology, TERI University, VasantKunj, New Delhi, 110070, India
| | - Aditi Singh
- Department of Biotechnology, TERI University, VasantKunj, New Delhi, 110070, India
| | - Salma Jamal
- Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India
| | - Jagdeep Kaur
- Department of Biotechnology, Panjab University, Chandigarh, 160014, India
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
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Singh A, Singh A, Grover S, Pandey B, Kumari A, Grover A. Wild-type catalase peroxidase vs G279D mutant type: Molecular basis of Isoniazid drug resistance in Mycobacterium tuberculosis. Gene 2018; 641:226-234. [DOI: 10.1016/j.gene.2017.10.047] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 10/16/2017] [Indexed: 11/29/2022]
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Aggarwal M, Singh A, Grover S, Pandey B, Kumari A, Grover A. Role of pncA gene mutations W68R and W68G in pyrazinamide resistance. J Cell Biochem 2017; 119:2567-2578. [PMID: 28980723 DOI: 10.1002/jcb.26420] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 10/03/2017] [Indexed: 12/12/2022]
Abstract
Mycobacterium tuberculosis (Mtb) resistance toward anti-tuberculosis drugs is a widespread problem. Pyrazinamide (PZA) is a first line antitubercular drug that kills semi-dormant bacilli when converted into its activated form, that is, pyrazinoic acid (POA) by Pyrazinamidase (PZase) enzyme coded by pncA gene. In this study, we conducted several analyses on native and mutant structures (W68R, W68G) of PZase before and after docking with the PZA drug to explore the molecular mechanism behind PZA resistance caused due to pncA mutations. Structural changes caused by mutations were studied with respect to their effects on functionality of protein. Docking was performed to analyze the protein-drug binding and comparative analysis was done to observe how the mutations affect drug binding affinity and binding site on protein. Native PZase protein was observed to have the maximum binding affinity in terms of docking score as well as shape complementarity in comparison to the mutant forms. Molecular dynamics simulation analyses showed that mutation in the 68th residue of protein results in a structural change at its active site which further affects the biological function of protein, that is, conversion of PZA to POA. Mutations in the protein thereby led to PZA resistance in the bacterium due to the inefficient binding.
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Affiliation(s)
- Mansi Aggarwal
- Ami, ty Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India
| | - Aditi Singh
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Department of Biotechnology, TERI University, Vasant Kunj, New Delhi, India
| | - Sonam Grover
- Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi, India
| | - Bharati Pandey
- Department of Biotechnology, Panjab University, Chandigarh, India
| | - Anchala Kumari
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Department of Biotechnology, TERI University, Vasant Kunj, New Delhi, India
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
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