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Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions
Wang, L (Wang, Lei)[ 1,2 ]; You, ZH (You, Zhu-Hong)[ 2 ]; Yan, X (Yan, Xin)[ 4 ]; Xia, SX (Xia, Shi-Xiong)[ 3 ]; Liu, F (Liu, Feng)[ 5 ]; Li, LP (Li, Li-Ping)[ 2 ]; Zhang, W (Zhang, Wei)[ 1 ]; Zhou, Y (Zhou, Yong)[ 3 ]
2018
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume8Issue:12874Pages:1-9
Abstract

The interaction among proteins is essential in all life activities, and it is the basis of all the metabolic activities of the cells. By studying the protein-protein interactions (PPIs), people can better interpret the function of protein, decoding the phenomenon of life, especially in the design of new drugs with great practical value. Although many high-throughput techniques have been devised for large-scale detection of PPIs, these methods are still expensive and time-consuming. For this reason, there is a much-needed to develop computational methods for predicting PPIs at the entire proteome scale. In this article, we propose a new approach to predict PPIs using Rotation Forest (RF) classifier combine with matrix-based protein sequence. We apply the Position-Specific Scoring Matrix (PSSM), which contains biological evolution information, to represent protein sequences and extract the features through the two-dimensional Principal Component Analysis (2DPCA) algorithm. The descriptors are then sending to the rotation forest classifier for classification. We obtained 97.43% prediction accuracy with 94.92% sensitivity at the precision of 99.93% when the proposed method was applied to the PPIs data of yeast. To evaluate the performance of the proposed method, we compared it with other methods in the same dataset, and validate it on an independent datasets. The results obtained show that the proposed method is an appropriate and promising method for predicting PPIs.

DOI10.1038/s41598-018-30694-1
Indexed BySCI
WOS IDWOS:000442870300052
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5557
Collection多语种信息技术研究室
Corresponding AuthorWang, L (Wang, Lei)[ 1,2 ]; You, ZH (You, Zhu-Hong)[ 2 ]
Affiliation1.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
4.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang 277100, Peoples R China
5.China Natl Coal Assoc, Beijing 100713, Peoples R China
Recommended Citation
GB/T 7714
Wang, L ,You, ZH ,Yan, X ,et al. Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions[J]. SCIENTIFIC REPORTS,2018,8(12874):1-9.
APA Wang, L .,You, ZH .,Yan, X .,Xia, SX .,Liu, F .,...&Zhou, Y .(2018).Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions.SCIENTIFIC REPORTS,8(12874),1-9.
MLA Wang, L ,et al."Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions".SCIENTIFIC REPORTS 8.12874(2018):1-9.
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