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Identifying Hosts of Families of Viruses: A Machine Learning Approach | | Anil Raj
; Michael Dewar
; Gustavo Palacios
; Raul Rabadan
; Chris H. Wiggins
; | | Date: |
29 May 2011 | | Abstract: | Identifying viral pathogens and characterizing their transmission is
essential to developing effective public health measures in response to a
pandemic. 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 pandemic. 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. | | Source: | arXiv, 1105.5821 | | Services: | Forum | Review | PDF | Favorites | |
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