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Public Library of Science, PLoS ONE, 12(6), p. e27631, 2011

DOI: 10.1371/journal.pone.0027631

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Identifying Hosts of Families of Viruses: A Machine Learning Approach

Journal article published in 2011 by Anil Raj, Michael Dewar, Gustavo Palacios ORCID, Raul Rabadan, Chris H. Wiggins
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Identifying emerging viral pathogens and characterizing their transmission is essential to developing effective public health measures in response to an epidemic. Phylogenetics, though currently the most popular tool used to characterize the likely host of a virus, can be ambiguous when studying species very distant to known species and when there is very little reliable sequence information available in the early stages of the outbreak of disease. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome.