Opening up the blackbox: An interpretable deep neural network-based classifier for cell-type specific enhancer predictions

BMC Systems Biology

Volumn 10, Issue , 2016, Pages

  Kim, Seong Gon ^a   Theera Ampornpunt, Nawanol ^a   Fang, Chih Hao ^a   Harwani, Mrudul ^a   Grama, Ananth ^a   Chaterji, Somali ^a  

^a Purdue University   (United States)

Author keywords

ChIP seq; Cis regulatory modules (CRMs); Deep neural networks (DNNs); Enhancer prediction; Genomic enhancers; Histone modifications; Interpretability of blackbox models

Indexed keywords

HISTONE;

ARTIFICIAL NEURAL NETWORK; BIOLOGY; ENHANCER REGION; GENE EXPRESSION REGULATION; GENETICS; HUMAN; METABOLISM; PROCEDURES; TUMOR CELL LINE;

CELL LINE, TUMOR; COMPUTATIONAL BIOLOGY; ENHANCER ELEMENTS, GENETIC; GENE EXPRESSION REGULATION; HISTONES; HUMANS; NEURAL NETWORKS (COMPUTER);

EID: 84979675872     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/s12918-016-0302-3     Document Type: Article

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