Open AccessCombining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants
Author(s) -
Xiang-He Meng,
HongMei Xiao,
HongWen Deng
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa970
Subject(s) - chromatin , computer science , computational biology , artificial intelligence , genome wide association study , source code , deep learning , single nucleotide polymorphism , machine learning , biology , genetics , gene , genotype , operating system
Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction.