InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution.

ABSTRACT: In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models 'Individual Meta RF' (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision-recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were c

SUBMITTER: Takeda JI 

PROVIDER: S-EPMC7671370 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature