Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges.

ABSTRACT: The steady advances in machine learning and accumulation of biomedical data have contributed to the development of numerous computational models that assess the impact of missense variants. Different methods, however, operationalize impact differently. Two common tasks in this context are the prediction of the pathogenicity of variants and the prediction of their effects on a protein's function. These are related but distinct problems, and it is unclear whether methods developed for one are optimized for the other. The Critical Assessment of Genome Interpretation (CAGI) experiment provides a means to address this question empirically. To this end, we participated in various protein-specific challenges in CAGI with two objectives in mind. First, to compare the performance of methods in the

SUBMITTER: Pejaver V 

PROVIDER: S-EPMC5561458 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature