Analysis and prediction of highly effective antiviral peptides based on random forests.
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ABSTRACT:
SUBMITTER: Chang KY
PROVIDER: S-EPMC3734225 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
Analysis and prediction of highly effective antiviral peptides based on random forests.
Chang Kuan Y KY Yang Je-Ruei JR
PloS one 20130805 8
The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficie ...[more]
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