Inferring protein interactions from sequence using support vector machine

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

doi:10.1109/IJCNN.2009.5178660

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 proceeding › Conference 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 language	English (US)
Title of host publication	2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages	2903-2907
Number of pages	5
DOIs	https://doi.org/10.1109/IJCNN.2009.5178660
State	Published - 2009
Externally published	Yes
Event	2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States Duration: Jun 14 2009 → Jun 19 2009

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/Territory	United States
City	Atlanta, GA
Period	6/14/09 → 6/19/09

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2009.5178660

Cite this

Inferring protein interactions from sequence using support vector machine. / Shi, Ming Guang; Wu, Min; Huang, De Shuang et al.
2009 International Joint Conference on Neural Networks, IJCNN 2009. 2009. p. 2903-2907 5178660 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shi, MG, Wu, M, Huang, DS & Li, XL 2009, Inferring protein interactions from sequence using support vector machine. in 2009 International Joint Conference on Neural Networks, IJCNN 2009., 5178660, Proceedings of the International Joint Conference on Neural Networks, pp. 2903-2907, 2009 International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, GA, United States, 6/14/09. https://doi.org/10.1109/IJCNN.2009.5178660

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title = "Inferring protein interactions from sequence using support vector machine",

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.",

author = "Shi, {Ming Guang} and Min Wu and Huang, {De Shuang} and Li, {Xue Ling}",

year = "2009",

doi = "10.1109/IJCNN.2009.5178660",

language = "English (US)",

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N2 - 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.

AB - 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.

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ER -

Inferring protein interactions from sequence using support vector machine

Abstract

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Other

ASJC Scopus subject areas

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