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Recombination HotspotColdspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach
Joint Authors
Liu, Bingquan
Huang, Dong
Liu, Yumeng
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-25
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Recombination presents a nonuniform distribution across the genome.
Genomic regions that present relatively higher frequencies of recombination are called hotspots while those with relatively lower frequencies of recombination are recombination coldspots.
Therefore, the identification of hotspots/coldspots could provide useful information for the study of the mechanism of recombination.
In this study, a new computational predictor called SVM-EL was proposed to identify hotspots/coldspots across the yeast genome.
It combined Support Vector Machines (SVMs) and Ensemble Learning (EL) based on three features including basic kmer (Kmer), dinucleotide-based auto-cross covariance (DACC), and pseudo dinucleotide composition (PseDNC).
These features are able to incorporate the nucleic acid composition and their order information into the predictor.
The proposed SVM-EL achieves an accuracy of 82.89% on a widely used benchmark dataset, which outperforms some related methods.
American Psychological Association (APA)
Liu, Bingquan& Liu, Yumeng& Huang, Dong. 2016. Recombination HotspotColdspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach. BioMed Research International،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099080
Modern Language Association (MLA)
Liu, Bingquan…[et al.]. Recombination HotspotColdspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach. BioMed Research International No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1099080
American Medical Association (AMA)
Liu, Bingquan& Liu, Yumeng& Huang, Dong. Recombination HotspotColdspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099080
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099080