Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants.

ABSTRACT: Disease and trait-associated variants represent a tiny minority of all known genetic variation, and therefore there is necessarily an imbalance between the small set of available disease-associated and the much larger set of non-deleterious genomic variation, especially in non-coding regulatory regions of human genome. Machine Learning (ML) methods for predicting disease-associated non-coding variants are faced with a chicken and egg problem - such variants cannot be easily found without ML, but ML cannot begin to be effective until a sufficient number of instances have been found. Most of state-of-the-art ML-based methods do not adopt specific imbalance-aware learning techniques to deal with imbalanced data that naturally arise in several genome-wide variant scoring problems, thus resulti

SUBMITTER: Schubach M 

PROVIDER: S-EPMC5462751 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature