Document Details

Document Type : Article In Journal 
Document Title :
iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework
iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework
 
Document Language : English 
Abstract : Motivation: Regulatory DNA elements are associated with DNase I hypersensitive sites (DHSs). Accordingly, identi􀂀cation 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) classi􀂀ers 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-ofthe- art methods in both accuracy and stability. Availability and Implementation: For the convenience of most experimental scientists, a web server for iDHS-EL is established at http://bioinformatics.hitsz.edu.cn/iDHS-EL, which is the 􀂀rst web-server predictor ever established for identifying DHSs, and by which users can easily get their desired results without the need to go through the mathematical details. We anticipate that iDHS-EL will become a very useful high throughput tool for genome analysis. 
ISSN : 1460-2059 
Journal Name : Bioinformatics 
Volume : 32 
Issue Number : 16 
Publishing Year : 1437 AH
2016 AD
 
Article Type : Article 
Added Date : Wednesday, July 12, 2017 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Liu BB, Liu ResearcherDoctorate 
Long RR, Long ResearcherDoctorate 
Chou KCKC, Chou ResearcherDoctorate 

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