FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model.

ABSTRACT: Single amino acid variants (SAVs) are the most abundant form of known genetic variations associated with human disease. Successful prediction of the functional impact of SAVs from sequences can thus lead to an improved understanding of the underlying mechanisms of why a SAV may be associated with certain disease. In this work, we constructed a high-quality structural dataset that contained 679 high-quality protein structures with 2,048 SAVs by collecting the human genetic variant data from multiple resources and dividing them into two categories, i.e., disease-associated and neutral variants. We built a two-stage random forest (RF) model, termed as FunSAV, to predict the functional effect of SAVs by combining sequence, structure and residue-contact network features with other additional fe

SUBMITTER: Wang M 

PROVIDER: S-EPMC3427247 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature