XJIPC OpenIR  > 多语种信息技术研究室
PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning
Li, JQ (Li, Jian-Qiang); You, ZH (You, Zhu-Hong); Li, X (Li, Xiao); Ming, Z (Ming, Zhong); Chen, X (Chen, Xing)
2017
Source PublicationIEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN1545-5963
Volume14Issue:5Pages:1165-1172
Abstract

Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs data has been generated by experimental methods, wet laboratory approaches are both time-consuming and costly. In addition, they yield high false negative and positive rates. Thus, there is a great need for in silico methods to predict SIPs accurately and efficiently. In this study, a new sequence-based method is proposed to predict SIPs. The evolutionary information contained in Position-Specific Scoring Matrix (PSSM) is extracted from of protein with known sequence. Then, features are fed to an ensemble classifier to distinguish the self-interacting and non-self-interacting proteins. When performed on Saccharomyces cerevisiae and Human SIPs data sets, the proposed method can achieve high accuracies of 86.86 and 91.30 percent, respectively. Our method also shows a good performance when compared with the SVM classifier and previous methods. Consequently, the proposed method can be considered to be a novel promising tool to predict SIPs.

KeywordSelf-interacting Proteins Ensemble Classifier Low Rank Protein Sequence
DOI10.1109/TCBB.2017.2649529
Indexed BySCI
WOS IDWOS:000418101500017
Citation statistics
Cited Times:20[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5084
Collection多语种信息技术研究室
Affiliation1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Guangdong, 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
Recommended Citation
GB/T 7714
Li, JQ ,You, ZH ,Li, X ,et al. PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2017,14(5):1165-1172.
APA Li, JQ ,You, ZH ,Li, X ,Ming, Z ,&Chen, X .(2017).PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,14(5),1165-1172.
MLA Li, JQ ,et al."PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 14.5(2017):1165-1172.
Files in This Item:
File Name/Size DocType Version Access License
PSPEL In Silico Pred(324KB)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
[Li, JQ (Li, Jian-Qiang)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Li, X (Li, Xiao)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, JQ (Li, Jian-Qiang)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Li, X (Li, Xiao)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, JQ (Li, Jian-Qiang)]'s Articles
[You, ZH (You, Zhu-Hong)]'s Articles
[Li, X (Li, Xiao)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: PSPEL In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

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