Document Details

Document Type : Article In Journal 
Document Title :
iRSpot-EL: identify recombination spots with an ensemble learning approach
iRSpot-EL: identify recombination spots with an ensemble learning approach
Document Language : English 
Abstract : Motivation: Coexisting in a DNA system, meiosis and recombination are two indispensible aspects for cell reproduction and growth. With the avalanche of genome sequences emerging in the postgenomic age, it is an urgent challenge to acquire the information of DNA recombination spots because it can timely provide very useful insights into the mechanism of meiotic recombination and the process of genome evolution. Results: To address such a challenge, we have developed a predictor, called iRSpot-EL, by fusing different modes of pseudo K-tuple nucleotide composition and mode of dinucleotide-based autocross covariance into an ensemble classifier of clustering approach. Five-fold cross tests on a widely used benchmark dataset have indicated that the new predictor remarkably outperforms its existing counterparts. Particularly, far beyond their reach, the new predictor can be easily used to conduct the genome-wide analysis and the results obtained are quite consistent with the experimental map. Availability and Implementation: For the convenience of most experimental scientists, a userfriendly web-server for iRSpot-EL has been established at EL/, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. 
ISSN : 1367-4803 
Journal Name : Bioinformatics 
Volume : 33 
Issue Number : 1 
Publishing Year : 1438 AH
2017 AD
Article Type : Article 
Added Date : Sunday, May 28, 2017 


Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Liu BinBin, Liu InvestigatorDoctorate 
Wang ShanyiShanyi, Wang ResearcherDoctorate 
Long RenRen, Long ResearcherDoctorate 
Chou Kuo-ChenKuo-Chen, Chou ResearcherDoctorate 


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