EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework | Zendy

Yangyang Hu | Zendy; Wenxiu Ma | Zendy

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EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework

Author(s) -

Yangyang Hu,

Wenxiu Ma

Publication year - 2021

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/btab272

Subject(s) - computer science , resolution (logic) , artificial intelligence , chromatin , rank (graph theory) , machine learning , data mining , pattern recognition (psychology) , dna , mathematics , biology , genetics , combinatorics

The high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem.

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