1. Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.
- Author
-
Dey L, Chakraborty S, and Mukhopadhyay A
- Subjects
- COVID-19 diagnosis, Humans, SARS-CoV-2, Sequence Analysis methods, COVID-19 virology, Host Microbial Interactions, Machine Learning, Proteins metabolism
- Abstract
Background: COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified., Method: In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad., Result: We have built an ensemble voting classifier using SVM
Radial , SVMPolynomial , and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported., Conclusion: This study may encourage the identification of potential targets for more effective anti-COVID drug discovery., Competing Interests: Conflicts of interest The authors declare that they have no conflict of interest., (Copyright © 2020 Chang Gung University. Published by Elsevier B.V. All rights reserved.)- Published
- 2020
- Full Text
- View/download PDF