2024학년도 2학기 의생명시스템학부 세미나를 안내해 드립니다.
•연사: 황규백 교수(숭실대학교 컴퓨터학부)
•제목: Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants
•초록: Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of genes related to a particular disease perform better than those trained using all variants, due to their higher specificity, despite the smaller training dataset size. In this study, we further investigated the advantages of “gene-specific” machine learning compared to “disease-specific” machine learning. We used 1068 rare (gnomAD minor allele frequency (MAF) < 0.005) missense variants of 28 genes associated with hereditary cancers for our investigation. Popular machine learning classifiers were employed: regularized logistic regression, extreme gradient boosting, random forests, support vector machines, and deep neural networks. As features, we used MAFs from multiple populations, functional prediction and conservation scores, and positions of variants. The disease-specific training dataset included the gene-specific training dataset and was > 7 × larger. However, we observed that gene-specific training variants were sufficient to produce the optimal pathogenicity predictor if a suitable machine learning classifier was employed. Therefore, we recommend gene-specific over disease-specific machine learning as an efficient and effective method for predicting the pathogenicity of rare BRCA1 and BRCA2 missense variants.
•일시: 10월 17일 목요일 오후 4시 30분
•장소: 벤처중소기업센터 711호
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