SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome.

ABSTRACT:

Summary

Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanced statistical and artificial learning approaches used by these methods make it difficult porting these methods outside of the labs that have developed them. To address these challenges and to transform the methodological advances in predicting deleterious noncoding mutations into a practical resour

SUBMITTER: Alvarez RV 

PROVIDER: S-EPMC5860207 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature