Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.

ABSTRACT: The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hi

SUBMITTER: Voskanian A 

PROVIDER: S-EPMC6744287 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature