Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.

ABSTRACT: Single amino acid variations (SAVs) potentially alter biological functions, including causing diseases or natural differences between individuals. Identifying the relationship between a SAV and certain disease provides the starting point for understanding the underlying mechanisms of specific associations, and can help further prevention and diagnosis of inherited disease.We propose PredSAV, a computational method that can effectively predict how likely SAVs are to be associated with disease by incorporating gradient tree boosting (GTB) algorithm and optimally selected neighborhood features. A two-step feature selection approach is used to explore the most relevant and informative neighborhood properties that contribute to the prediction of disease association of SAVs across a wide range o

SUBMITTER: Pan Y 

PROVIDER: S-EPMC5470696 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature