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RegVar: Tissue-specific Prioritization of Noncoding Regulatory Variants
[article]
2021
bioRxiv
pre-print
Noncoding genomic variants constitute the majority of trait-associated genome variations; however, identification of functional noncoding variants is still a challenge in human genetics, and a method systematically assessing the impact of regulatory variants on gene expression and linking them to potential target genes is still lacking. Here we introduce a deep neural network (DNN)-based computational framework, RegVar, that can accurately predict the tissue-specific impact of noncoding
doi:10.1101/2021.04.17.440295
fatcat:qnotgpejtjgqhf7g2wxzc5daum