title: Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots.
abstract: Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn and Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Chuan
authors.initials: C
authors.lastname: Dong
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn and Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Ya-Zhou
authors.initials: YZ
authors.lastname: Yuan
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn and Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Fa-Zhan
authors.initials: FZ
authors.lastname: Zhang
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn and Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Hong-Li
authors.initials: HL
authors.lastname: Hua
authors.affiliation: School of Biology and Engineering, Guizhou Medical University, Guiyang, China.
authors.forename: Yuan-Nong
authors.initials: YN
authors.lastname: Ye
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn and Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Abraham Alemayehu
authors.initials: AA
authors.lastname: Labena
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn and Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.affiliation: Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. fbguo@uestc.edu.cn and Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Feng-Biao
authors.initials: FB
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D016681:Genome, Fungal; D009711:Nucleotides; D012372:ROC Curve; D011995:Recombination, Genetic; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D064878:Web Browser
chemical_list: D009711:Nucleotides; D004247:DNA