Journal
BIOINFORMATICS
Volume 32, Issue 16, Pages 2411-2418Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btw186
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Funding
- National Natural Science Foundation of China [61300112, 61573118, 61272383]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
- Natural Science Foundation of Guangdong Province [2014A030313695]
- Shenzhen Foundational Research Funding [JCYJ20150626110425228]
- Development Program of China (863 Program) [2015AA015405]
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Motivation: Regulatory DNA elements are associated with DNase I hypersensitive sites (DHSs). Accordingly, identification of DHSs will provide useful insights for in-depth investigation into the function of noncoding genomic regions. Results: In this study, using the strategy of ensemble learning framework, we proposed a new predictor called iDHS-EL for identifying the location of DHS in human genome. It was formed by fusing three individual Random Forest (RF) classifiers into an ensemble predictor. The three RF operators were respectively based on the three special modes of the general pseudo nucleotide composition (PseKNC): (i) kmer, (ii) reverse complement kmer and (iii) pseudo dinucleotide composition. It has been demonstrated that the new predictor remarkably outperforms the relevant state-of-the-art methods in both accuracy and stability.
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