Improved pathogenicity prediction for rare human missense variants
Yingzhou Wu, Hanqing Liu, Roujia Li, Song Sun, Jochen Weile, Frederick P. Roth
Abstract
(The American Journal of Human Genetics 108, 1891–1906; October 7, 2021) In the originally published version of this article, Hanqing Liu was omitted from the author list. The author list has now been corrected online and includes Hanqing Liu and his current affiliations. The authors regret the error. Improved pathogenicity prediction for rare human missense variantsWu et al.The American Journal of Human GeneticsSeptember 21, 2021In BriefThe success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. Full-Text PDF Open Access