Predicting cytokines based on dipeptide and length feature

Wei He, Zhenran Jiang, Zhibin Li

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

2 Scopus citations

Abstract

Cytokines are crucial intercellular regulators that have important physiological roles in a wide range of disease processes. The identification of new cytokines by computational methods can provide valuable clues in functional studies of uncharacterized proteins without performing extensive experiments. In this study, we developed a new prediction method for the cytokine family based on dipeptide composition and length distribution by using support vector machine (SVM). The cross-validation results demonstrated that cytokines could be correctly identified with an accuracy of 97% at family classification and 90% at subfamily recognition correctly, respectively. In comparison with existing methods in the literature, the present method displayed great competitiveness on identifying cytokines correctly.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings
Pages86-91
Number of pages6
DOIs
StatePublished - 2008
Event4th International Conference on Intelligent Computing, ICIC 2008 - Shanghai, China
Duration: 15 Sep 200818 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Intelligent Computing, ICIC 2008
Country/TerritoryChina
CityShanghai
Period15/09/0818/09/08

Keywords

  • Dipeptide composition
  • Multi-class classification
  • SVM

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