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Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier
Wang, L (Wang, Lei); You, ZH (You, Zhu-Hong); Xia, SX (Xia, Shi-Xiong); Liu, F (Liu, Feng); Chen, X (Chen, Xing); Yan, X (Yan, Xin); Zhou, Y (Zhou, Yong)
2017
Source PublicationJOURNAL OF THEORETICAL BIOLOGY
ISSN0022-5193
Volume418Issue:4Pages:105-110
Abstract

Protein-Protein Interactions (PPIs) are essential to most biological processes and play a critical role in most cellular functions. With the development of high-throughput biological techniques and in si/ico methods, a large number of PPI data have been generated for various organisms, but many problems remain unsolved. These factors promoted the development of the in silico methods based on machine learning to predict PPIs. In this study, we propose a novel method by combining ensemble Rotation Forest (RF) classifier and Discrete Cosine Transform (DCT) algorithm to predict the interactions among proteins. Specifically, the protein amino acids sequence is transformed into Position-Specific Scoring Matrix (PSSM) containing biological evolution information, and then the feature vector is extracted to present protein evolutionary information using DCT algorithm; finally, the ensemble rotation forest model is used to predict whether a given protein pair is interacting or not. When performed on Yeast and H. pylori data sets, the proposed method achieved excellent results with an average accuracy of 98.54% and 88.27%. In addition, we achieved good prediction accuracy of 98.08%, 92.75%, 98.87% and 98.72% on independent data sets (C.elegans, E.coli, Hsapiens and M.muscu/us). In order to further evaluate the performance of our method, we compare it with the state-of-the-art Support Vector Machine (SVM) classifier and get good results.

KeywordPosition-specific Scoring Matrix Multiple Sequences Alignments Rotation Forest Cancer
DOI10.1016/j.jtbi.2017.01.003
Indexed BySCI
WOS IDWOS:000397701300011
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/4767
Collection多语种信息技术研究室
Affiliation1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
2.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Shandong, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
4.China Natl Coal Assoc, Beijing 100713, Peoples R China
5.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China
6.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang 277100, Shandong, Peoples R China
Recommended Citation
GB/T 7714
Wang, L ,You, ZH ,Xia, SX ,et al. Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier[J]. JOURNAL OF THEORETICAL BIOLOGY,2017,418(4):105-110.
APA Wang, L .,You, ZH .,Xia, SX .,Liu, F .,Chen, X .,...&Zhou, Y .(2017).Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier.JOURNAL OF THEORETICAL BIOLOGY,418(4),105-110.
MLA Wang, L ,et al."Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier".JOURNAL OF THEORETICAL BIOLOGY 418.4(2017):105-110.
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