Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants | Zendy

Xiang-He Meng | Zendy; HongMei Xiao | Zendy; HongWen Deng | Zendy

Open Access

Combining 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 , computational biology , computer science , genome wide association study , source code , artificial intelligence , deep learning , coding (social sciences) , single nucleotide polymorphism , machine learning , biology , genetics , gene , genotype , operating system , statistics , mathematics

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.

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