Inferring protein interactions from sequence using support vector machine

Ming Guang Shi, Min Wu, De Shuang Huang, Xue Ling Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data of protein-protein interactions derived from High-throughput technologies are often incomplete and fairly noisy. Therefore, it is very important to develop computational methods for predicting protein-protein interactions. A sequence-based method is proposed by combining support vector machine and a new feature representation using Geary autocorrelation. SVM model trained with Geary autocorrelation of amino acid sequence yielded the best performance with a high accuracy of 82.9% using gold standard positives (GSPs) PRS and gold standard negatives (GSNs) RRS datasets. Meanwhile, the SVM model has been successfully employed to predict the single core PPI network.

Original languageEnglish (US)
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages2903-2907
Number of pages5
DOIs
StatePublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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