A SVM-based system for predicting protein-protein interactions using a novel representation of protein sequences

Zhuhong You, Zhong Ming, Ben Niu, Suping Deng, Zexuan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. However, the experimental methods for identifying PPIs are both time-consuming and expensive. Therefore, it is important to develop computational approaches for predicting PPIs. In this article, a sequence-based method is developed by combining a novel feature representation using binary coding and Support Vector Machine (SVM). The binary-coding-based descriptors account for the interactions between residues a certain distance apart in the protein sequence, thus this method adequately takes the neighboring effect into account and mine interaction information from the continuous and discontinuous amino acids segments at the same time. When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 86.93% prediction accuracy with 86.99% sensitivity at the precision of 86.90%. Extensive experiments are performed to compare our method with the existing sequence-based method. Achieved results show that the proposed approach is very promising for predicting PPI, so it can be a useful supplementary tool for future proteomics studies.

Original languageEnglish
Title of host publicationIntelligent Computing Theories - 9th International Conference, ICIC 2013, Proceedings
Pages629-637
Number of pages9
DOIs
StatePublished - 2013
Externally publishedYes
Event9th International Conference on Intelligent Computing, ICIC 2013 - Nanning, China
Duration: 28 Jul 201331 Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7995 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Intelligent Computing, ICIC 2013
Country/TerritoryChina
CityNanning
Period28/07/1331/07/13

Keywords

  • binary coding
  • local descriptor
  • protein sequence
  • protein-protein interaction
  • support vector machine

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