Inferring protein interactions from sequence using support vector machine

Authors:
Ming-Guang Shi;Min Wu;De-Shuang Huang;Xue-Ling Li
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China and University of Science and Technology of China and Hefei University of Technology;Chinese Academy of Sciences and the Hefei University of Technology;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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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 82.90% 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.