Computational Prediction of the Pathogenic Status of Cancer-Specific Somatic Variants.

ABSTRACT: In-silico classification of the pathogenic status of somatic variants is shown to be promising in promoting the clinical utilization of genetic tests. Majority of the available classification tools are designed based on the characteristics of germline variants or the combination of germline and somatic variants. Significance of somatic variants in cancer initiation and progression urges for development of classifiers specialized for classifying pathogenic status of cancer somatic variants based on the model trained on cancer somatic variants. We established a gold standard exclusively for cancer somatic single nucleotide variants (SNVs) collected from the catalogue of somatic mutations in cancer. We developed two support vector machine (SVM) classifiers based on genomic features of

SUBMITTER: Feizi N 

PROVIDER: S-EPMC8804317 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature