title: Identifying hosts of families of viruses: a machine learning approach.
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.
authors.affiliation: Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York, United States of America. ar2384@columbia.edu
authors.forename: Anil
authors.initials: A
authors.lastname: Raj
authors.forename: Michael
authors.initials: M
authors.lastname: Dewar
authors.forename: Gustavo
authors.initials: G
authors.lastname: Palacios
authors.forename: Raul
authors.initials: R
authors.lastname: Rabadan
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Wiggins
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001483:Base Sequence; D017124:Conserved Sequence; D058507:Host Specificity / Q000502:physiology*; D008969:Molecular Sequence Data; D015203:Reproducibility of Results; D016415:Sequence Alignment; D018406:Virus Physiological Phenomena; D014780:Viruses / Q000145:classification* / Q000235:genetics / Q000472:pathogenicity