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An improved efficient rotation forest algorithm to predict the interactions among proteins
Wang, L (Wang, Lei); You, ZH (You, Zhu-Hong); Xia, SX (Xia, Shi-Xiong); Chen, X (Chen, Xing); Yan, X (Yan, Xin); Zhou, Y (Zhou, Yong); Liu, F (Liu, Feng)
2018
Source PublicationSOFT COMPUTING
Volume22Issue:10Pages:3373-3381
Abstract

Protein-protein interactions (PPIs) are the basis to interpret biological mechanisms of life activity, and play vital roles in the execution of various cellular processes. The development of computer technology provides a new way for the effective prediction of PPIs and greatly arouses people's interest. The challenge of this task is that PPIs data is typically represented in high-dimensional and is likely to contain noise, which will greatly affect the performance of the classifier. In this paper, we propose a novel feature weighted rotation forest algorithm (FWRF) to solve this problem. We calculate the weight of the feature by the chi(2) statistical method and remove the low weight value features according to the selection rate. With this FWRF algorithm, the proposed method can eliminate the interference of useless information and make full use of the useful features to predict the interactions among proteins. In cross-validation experiment, our method obtained excellent prediction performance with the average accuracy, precision, sensitivity, MCC and AUC of 91.91, 92.51, 91.22, 83.84 and 91.60% on the H. pylori data set. We compared our method with other existing methods and the well-known classifiers, such as SVM and original rotation forest on the H. pylori data set. In addition, in order to demonstrate the ability of the FWRF algorithm, we also verified on the Yeast data set. The experimental results show that our method is more effective and robust in predicting PPIs. As a web server, the source code, H. pylori data sets and Yeast data sets used in this article are freely available at http://202.119.201.126:8888/FWRF/.

KeywordRotation Forest Ensemble Learning Bioinformatics Support Vector Machine
DOI10.1007/s00500-017-2582-y
Indexed BySCI
WOS IDWOS:000431174800018
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5411
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)
Affiliation1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China
4.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang 277100, Shandong, Peoples R China
5.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Shandong, Peoples R China
6.China Natl Coal Assoc, Beijing 100713, Peoples R China
Recommended Citation
GB/T 7714
Wang, L ,You, ZH ,Xia, SX ,et al. An improved efficient rotation forest algorithm to predict the interactions among proteins[J]. SOFT COMPUTING,2018,22(10):3373-3381.
APA Wang, L .,You, ZH .,Xia, SX .,Chen, X .,Yan, X .,...&Liu, F .(2018).An improved efficient rotation forest algorithm to predict the interactions among proteins.SOFT COMPUTING,22(10),3373-3381.
MLA Wang, L ,et al."An improved efficient rotation forest algorithm to predict the interactions among proteins".SOFT COMPUTING 22.10(2018):3373-3381.
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