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Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has chal...

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Autores principales: Bhat, Shikha, Pandey, Anuradha, Kanakan, Akshay, Maurya, Ranjeet, Vasudevan, Janani Srinivasa, Devi, Priti, Chattopadhyay, Partha, Sharma, Shimpa, Khyalappa, Rajesh J., Joshi, Meghnad G., Pandey, Rajesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762993/
https://www.ncbi.nlm.nih.gov/pubmed/35047415
http://dx.doi.org/10.3389/fcimb.2021.783961
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author Bhat, Shikha
Pandey, Anuradha
Kanakan, Akshay
Maurya, Ranjeet
Vasudevan, Janani Srinivasa
Devi, Priti
Chattopadhyay, Partha
Sharma, Shimpa
Khyalappa, Rajesh J.
Joshi, Meghnad G.
Pandey, Rajesh
author_facet Bhat, Shikha
Pandey, Anuradha
Kanakan, Akshay
Maurya, Ranjeet
Vasudevan, Janani Srinivasa
Devi, Priti
Chattopadhyay, Partha
Sharma, Shimpa
Khyalappa, Rajesh J.
Joshi, Meghnad G.
Pandey, Rajesh
author_sort Bhat, Shikha
collection PubMed
description The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.
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spelling pubmed-87629932022-01-18 Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification Bhat, Shikha Pandey, Anuradha Kanakan, Akshay Maurya, Ranjeet Vasudevan, Janani Srinivasa Devi, Priti Chattopadhyay, Partha Sharma, Shimpa Khyalappa, Rajesh J. Joshi, Meghnad G. Pandey, Rajesh Front Cell Infect Microbiol Cellular and Infection Microbiology The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model. Frontiers Media S.A. 2021-12-24 /pmc/articles/PMC8762993/ /pubmed/35047415 http://dx.doi.org/10.3389/fcimb.2021.783961 Text en Copyright © 2021 Bhat, Pandey, Kanakan, Maurya, Vasudevan, Devi, Chattopadhyay, Sharma, Khyalappa, Joshi and Pandey https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular and Infection Microbiology
Bhat, Shikha
Pandey, Anuradha
Kanakan, Akshay
Maurya, Ranjeet
Vasudevan, Janani Srinivasa
Devi, Priti
Chattopadhyay, Partha
Sharma, Shimpa
Khyalappa, Rajesh J.
Joshi, Meghnad G.
Pandey, Rajesh
Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification
title Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification
title_full Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification
title_fullStr Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification
title_full_unstemmed Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification
title_short Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification
title_sort learning from biological and computational machines: importance of sars-cov-2 genomic surveillance, mutations and risk stratification
topic Cellular and Infection Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762993/
https://www.ncbi.nlm.nih.gov/pubmed/35047415
http://dx.doi.org/10.3389/fcimb.2021.783961
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