XJIPC OpenIR  > 多语种信息技术研究室
Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
Wang, YB (Wang, Yan-Bin)1; You, ZH (You, Zhu-Hong)1; Li, LP (Li, Li-Ping)1; Huang, YA (Huang, Yu-An)1; Yi, HC (Yi, Hai-Cheng)1
2017
Source PublicationMOLECULES
ISSN1420-3049
Volume22Issue:8Pages:1-13
Abstract

Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research.

KeywordProtein-protein Interactions Legendre Moments Position Specific Scoring Matrix Probabilistic Classification Vector Machine
DOI10.3390/molecules22081366
Indexed BySCI
WOS IDWOS:000408602900129
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/4929
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)
Affiliation1.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Wang, YB ,You, ZH ,Li, LP ,et al. Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM[J]. MOLECULES,2017,22(8):1-13.
APA Wang, YB ,You, ZH ,Li, LP ,Huang, YA ,&Yi, HC .(2017).Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.MOLECULES,22(8),1-13.
MLA Wang, YB ,et al."Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM".MOLECULES 22.8(2017):1-13.
Files in This Item:
File Name/Size DocType Version Access License
Detection of Interac(977KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, YB (Wang, Yan-Bin)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Li, LP (Li, Li-Ping)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, YB (Wang, Yan-Bin)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Li, LP (Li, Li-Ping)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, YB (Wang, Yan-Bin)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Li, LP (Li, Li-Ping)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.