MVP: predicting pathogenicity of missense variants by deep learning

Autor: Yufeng Shen, Haicang Zhang, Yongtao Guan, Hongjian Qi, John J. Long, Wendy K. Chung, Chen Chen
Jazyk: angličtina
Rok vydání: 2018
Předmět:
DOI: 10.1101/259390
Popis: Accurate pathogenicity prediction of missense variants is critical to improve power in genetic studies and accurate interpretation in clinical genetic testing. Here we describe a new prediction method, MVP, which uses a deep learning approach to leverage large training data sets and many correlated predictors. Using cancer mutation hotspots and de novo germline mutations from developmental disorders for benchmarking, MVP achieved better performance in prioritizing pathogenic missense variants than previous methods.
Databáze: OpenAIRE