BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone
Author(s) -
Bite Yang,
Feng Liu,
Chao Ren,
Zhangyi Ouyang,
Ziwei Xie,
Xiaochen Bo,
Wenjie Shu
Publication year - 2017
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/btx105
Subject(s) - enhancer , computational biology , generalizability theory , robustness (evolution) , sequence (biology) , dna sequencing , computer science , biology , dna , gene , genetics , transcription factor , mathematics , statistics
Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent.
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