期刊
BMC GENOMICS
卷 19, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12864-018-5283-8
关键词
DNase I hypersensitive sites; Deep learning; Convolutional neural network
资金
- open project of Key Laboratory of Convergence Medical Engineering System and Healthcare Technology of the Ministry of Industry and Information Technology, Beijing institute of technology, China
BackgroundThe DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.MethodsHere, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.ResultsOur method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.ConclusionsOur method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.
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