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Forna A, Weedop KB, Damodaran L, Hassell N, Kondor R, Bahl J, Drake JM, Rohani P. Sequence-based detection of emerging antigenically novel influenza A viruses. Proc Biol Sci 2024; 291:20240790. [PMID: 39140324 PMCID: PMC11323087 DOI: 10.1098/rspb.2024.0790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 05/21/2024] [Accepted: 07/11/2024] [Indexed: 08/15/2024] Open
Abstract
The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.
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Affiliation(s)
- Alpha Forna
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - K. Bodie Weedop
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
| | - Lambodhar Damodaran
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - Norman Hassell
- Centers for Disease Control and Prevention, Atlanta, GA30329, USA
| | - Rebecca Kondor
- Centers for Disease Control and Prevention, Atlanta, GA30329, USA
| | - Justin Bahl
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Center for Influenza Disease & Emergence Research (CIDER), Athens, GA30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Center for Influenza Disease & Emergence Research (CIDER), Athens, GA30602, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA30602, USA
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2
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Hayati M, Sobkowiak B, Stockdale JE, Colijn C. Phylogenetic identification of influenza virus candidates for seasonal vaccines. SCIENCE ADVANCES 2023; 9:eabp9185. [PMID: 37922357 PMCID: PMC10624341 DOI: 10.1126/sciadv.abp9185] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 10/05/2023] [Indexed: 11/05/2023]
Abstract
The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.
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Affiliation(s)
- Maryam Hayati
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Benjamin Sobkowiak
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | | | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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3
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Marquez E, Barrón-Palma EV, Rodríguez K, Savage J, Sanchez-Sandoval AL. Supervised Machine Learning Methods for Seasonal Influenza Diagnosis. Diagnostics (Basel) 2023; 13:3352. [PMID: 37958248 PMCID: PMC10647880 DOI: 10.3390/diagnostics13213352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza's relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.
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Affiliation(s)
- Edna Marquez
- Genomic Medicine Department, General Hospital of México “Dr. Eduardo Liceaga”, Mexico City 06726, Mexico; (E.V.B.-P.)
| | - Eira Valeria Barrón-Palma
- Genomic Medicine Department, General Hospital of México “Dr. Eduardo Liceaga”, Mexico City 06726, Mexico; (E.V.B.-P.)
| | - Katya Rodríguez
- Institute for Research in Applied Mathematics and Systems, National Autonomous University of Mexico, Mexico City 04510, Mexico;
| | - Jesus Savage
- Signal Processing Department, Engineering School, National Autonomous University of Mexico, Mexico City 04510, Mexico;
| | - Ana Laura Sanchez-Sandoval
- Genomic Medicine Department, General Hospital of México “Dr. Eduardo Liceaga”, Mexico City 06726, Mexico; (E.V.B.-P.)
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4
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Khalaj-Hedayati A, Moosavi S, Manta O, Helal MH, Ibrahim MM, El-Bahy ZM, Supriyanto G. Identification and In Silico Characterization of a Conserved Peptide on Influenza Hemagglutinin Protein: A New Potential Antigen for Universal Influenza Vaccine Development. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2796. [PMID: 37887946 PMCID: PMC10609762 DOI: 10.3390/nano13202796] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
Antigenic changes in surface proteins of the influenza virus may cause the emergence of new variants that necessitate the reformulation of influenza vaccines every year. Universal influenza vaccine that relies on conserved regions can potentially be effective against all strains regardless of any antigenic changes and as a result, it can bring enormous public health impact and economic benefit worldwide. Here, a conserved peptide (HA288-107) on the stalk domain of hemagglutinin glycoprotein is identified among highly pathogenic influenza viruses. Five top-ranked B-cell and twelve T-cell epitopes were recognized by epitope mapping approaches and the corresponding Human Leukocyte Antigen alleles to T-cell epitopes showed high population coverage (>99%) worldwide. Moreover, molecular docking analysis indicated that VLMENERTL and WTYNAELLV epitopes have high binding affinity to the antigen-binding groove of the HLA-A*02:01 and HLA-A*68:02 molecules, respectively. Theoretical physicochemical properties of the peptide were assessed to ensure its thermostability and hydrophilicity. The results suggest that the HA288-107 peptide can be a promising antigen for universal influenza vaccine design. However, in vitro and in vivo analyses are needed to support and evaluate the effectiveness of the peptide as an immunogen for vaccine development.
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Affiliation(s)
- Atin Khalaj-Hedayati
- Department of Chemistry, Faculty of Science and Technology, Airlangga University, Mulyorejo, Surabaya 60115, Indonesia
- School of Biosciences, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya 47500, Malaysia
| | - Seyedehmaryam Moosavi
- Department of Nanotechnology Engineering, Faculty of Advance Technology and Multidiscipline, Airlangga University, Mulyorejo, Surabaya 60115, Indonesia;
| | - Otilia Manta
- Romanian Academy, Victor Slavescu Centre for Financial and Monetary Research, 050731 Bucharest, Romania;
- Romanian Academy, CE-MONT Mountain Economy Center, 725700 Vatra Dornei, Romania
- Research Department, Romanian American University, 012101 Bucharest, Romania
| | - Mohamed H. Helal
- Department of Chemistry, Faculty of Arts and Science, Northern Border University, Rafha 76413, Saudi Arabia;
| | - Mohamed M. Ibrahim
- Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Zeinhom M. El-Bahy
- Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City 11884, Egypt;
| | - Ganden Supriyanto
- Department of Chemistry, Faculty of Science and Technology, Airlangga University, Mulyorejo, Surabaya 60115, Indonesia
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5
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Charlebois DA. Quantitative systems-based prediction of antimicrobial resistance evolution. NPJ Syst Biol Appl 2023; 9:40. [PMID: 37679446 PMCID: PMC10485028 DOI: 10.1038/s41540-023-00304-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.
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Affiliation(s)
- Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, AB, T6G-2E1, Canada.
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G-2E9, Canada.
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6
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Wortel MT, Agashe D, Bailey SF, Bank C, Bisschop K, Blankers T, Cairns J, Colizzi ES, Cusseddu D, Desai MM, van Dijk B, Egas M, Ellers J, Groot AT, Heckel DG, Johnson ML, Kraaijeveld K, Krug J, Laan L, Lässig M, Lind PA, Meijer J, Noble LM, Okasha S, Rainey PB, Rozen DE, Shitut S, Tans SJ, Tenaillon O, Teotónio H, de Visser JAGM, Visser ME, Vroomans RMA, Werner GDA, Wertheim B, Pennings PS. Towards evolutionary predictions: Current promises and challenges. Evol Appl 2023; 16:3-21. [PMID: 36699126 PMCID: PMC9850016 DOI: 10.1111/eva.13513] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 12/14/2022] Open
Abstract
Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.
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Affiliation(s)
- Meike T. Wortel
- Swammerdam Institute for Life SciencesUniversity of AmsterdamAmsterdamThe Netherlands
| | - Deepa Agashe
- National Centre for Biological SciencesBangaloreIndia
| | | | - Claudia Bank
- Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Gulbenkian Science InstituteOeirasPortugal
| | - Karen Bisschop
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
- Laboratory of Aquatic Biology, KU Leuven KulakKortrijkBelgium
| | - Thomas Blankers
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
- Origins CenterGroningenThe Netherlands
| | | | - Enrico Sandro Colizzi
- Origins CenterGroningenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | | | - Bram van Dijk
- Max Planck Institute for Evolutionary BiologyPlönGermany
| | - Martijn Egas
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | - Jacintha Ellers
- Department of Ecological ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Astrid T. Groot
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | | | | | - Ken Kraaijeveld
- Leiden Centre for Applied BioscienceUniversity of Applied Sciences LeidenLeidenThe Netherlands
| | - Joachim Krug
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Liedewij Laan
- Department of Bionanoscience, Kavli Institute of NanoscienceTU DelftDelftThe Netherlands
| | - Michael Lässig
- Institute for Biological PhysicsUniversity of CologneCologneGermany
| | - Peter A. Lind
- Department Molecular BiologyUmeå UniversityUmeåSweden
| | - Jeroen Meijer
- Theoretical Biology and Bioinformatics, Department of BiologyUtrecht UniversityUtrechtThe Netherlands
| | - Luke M. Noble
- Institute de Biologie, École Normale Supérieure, CNRS, InsermParisFrance
| | | | - Paul B. Rainey
- Department of Microbial Population BiologyMax Planck Institute for Evolutionary BiologyPlönGermany
- Laboratoire Biophysique et Évolution, CBI, ESPCI Paris, Université PSL, CNRSParisFrance
| | - Daniel E. Rozen
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | - Shraddha Shitut
- Origins CenterGroningenThe Netherlands
- Institute of Biology, Leiden UniversityLeidenThe Netherlands
| | | | | | | | | | - Marcel E. Visser
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)WageningenThe Netherlands
| | - Renske M. A. Vroomans
- Origins CenterGroningenThe Netherlands
- Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands
| | | | - Bregje Wertheim
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
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7
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Hayati M, Chindelevitch L, Aanensen D, Colijn C. Deep clustering of bacterial tree images. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210231. [PMID: 35989604 PMCID: PMC9393560 DOI: 10.1098/rstb.2021.0231] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 05/17/2022] [Indexed: 01/25/2023] Open
Abstract
The field of genomic epidemiology is rapidly growing as many jurisdictions begin to deploy whole-genome sequencing (WGS) in their national or regional pathogen surveillance programmes. WGS data offer a rich view of the shared ancestry of a set of taxa, typically visualized with phylogenetic trees illustrating the clusters or subtypes present in a group of taxa, their relatedness and the extent of diversification within and between them. When methicillin-resistant Staphylococcus aureus (MRSA) arose and disseminated widely, phylogenetic trees of MRSA-containing types of S. aureus had a distinctive 'comet' shape, with a 'comet head' of recently adapted drug-resistant isolates in the context of a 'comet tail' that was predominantly drug-sensitive. Placing an S. aureus isolate in the context of such a 'comet' helped public health laboratories interpret local data within the broader setting of S. aureus evolution. In this work, we ask what other tree shapes, analogous to the MRSA comet, are present in bacterial WGS datasets. We extract trees from large bacterial genomic datasets, visualize them as images and cluster the images. We find nine major groups of tree images, including the 'comets', star-like phylogenies, 'barbell' phylogenies and other shapes, and comment on the evolutionary and epidemiological stories these shapes might illustrate. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
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Affiliation(s)
- Maryam Hayati
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6
| | - Leonid Chindelevitch
- Department of Infectious Disease Epidemiology, Imperial College, Praed Street, London W2 1NY, UK
| | - David Aanensen
- Big Data Institute, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6
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8
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OUP accepted manuscript. Syst Biol 2022; 71:1378-1390. [DOI: 10.1093/sysbio/syac008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 02/05/2022] [Accepted: 02/08/2022] [Indexed: 11/12/2022] Open
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9
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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10
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Bansal A, Padappayil RP, Garg C, Singal A, Gupta M, Klein A. Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review. J Med Syst 2020; 44:156. [PMID: 32740678 PMCID: PMC7395799 DOI: 10.1007/s10916-020-01617-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/15/2020] [Indexed: 01/07/2023]
Abstract
The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.
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Affiliation(s)
- Agam Bansal
- Internal Medicine, Cleveland Clinic, Cleveland, OH USA
| | | | - Chandan Garg
- Deptartment of Statistics, Columbia University, New York, NY USA
| | - Anjali Singal
- Deptartment of Anatomy, All India Institute of Medical Sciences, Bathinda, India
| | - Mohak Gupta
- All India Institute of Medical Sciences, New Delhi, India
| | - Allan Klein
- Deptartment of Cardiology, Cleveland Clinic, Cleveland, OH USA
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