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• W2991566019 abstract "Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for maximizing the knowledge dividend from human genome-wide association studies (GWAS). Recently, diverse machine learning-based methods have been used for functional SNP identification; however, this task remains a fundamental challenge in computational biology. We report CERENKOV3, a machine learning pipeline that leverages clustering-derived and molecular network-derived features to improve prediction accuracy of regulatory SNPs (rSNPs) in the context of post-GWAS analysis. The clustering-derived feature, locus size (number of SNPs in the locus), derives from our locus partitioning procedure and represents the sizes of clusters based on SNP locations. We generated two molecular network-derived features from representation learning on a network representing SNP-gene and gene-gene relations. Based on empirical studies using a ground-truth SNP dataset, CERENKOV3 significantly improves rSNP recognition performance in AUPRC, AUROC, and AVGRANK (a locus-wise rank-based measure of classification accuracy we previously proposed)." @default.
• W2991566019 created "2019-12-05" @default.
• W2991566019 creator A5045197865 @default.
• W2991566019 creator A5091354698 @default.
• W2991566019 date "2019-11-27" @default.
• W2991566019 modified "2023-09-23" @default.
• W2991566019 title "CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs" @default.
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• W2991566019 doi "https://doi.org/10.1142/9789811215636_0047" @default.
• W2991566019 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6897322" @default.
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• W2991566019 hasPublicationYear "2019" @default.
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