SVM-based prediction of protein-protein interactions of Glucosinolate biosynthesis

Yan Shuo Chu; Ya Qiu Liu; Qu Wu

doi:10.1109/ICMLC.2012.6358969

SVM-based prediction of protein-protein interactions of Glucosinolate biosynthesis

Yan Shuo Chu
, Ya Qiu Liu^*
, Qu Wu

^*Corresponding author for this work

Northeast Forestry University

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

1 Scopus citations

Abstract

Protein-protein interactions (PPIs) are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more PPIs of Glucosinolates biosynthetic pathways and removing PPIs falsely predicted. A support vector machine (SVM) predictor with the radial basis kernel function (RBF kernel) is trained based on the domain and domain-domain interaction (DDI) information of the amino acid sequences. In this paper, a symmetrical pair of feature vectors is used to represent the symmetrical relationship between two proteins, and 5-fold cross-validation is used to search the best SVM parameters. Then the best SVM parameters are used to train the SVM-based PPIs predictor. The proteins originate from gene AT4G14800 and ATSGS4810 (ID of Arabidopsis Genome Initiative (AGI)), ATSGOS730 and AT4G18040, ATlG04S10 and ATSGOS260 are affirmed interactive by this SVM-based PPIs predictor.

Original language	English
Title of host publication	Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Publisher	IEEE Computer Society
Pages	471-476
Number of pages	6
ISBN (Print)	9781467314855
DOIs	https://doi.org/10.1109/ICMLC.2012.6358969
State	Published - 2012
Externally published	Yes
Event	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China Duration: 15 Jul 2012 → 17 Jul 2012

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	2
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Country/Territory	China
City	Xian, Shaanxi
Period	15/07/12 → 17/07/12

Keywords

domain-domain interactions (DDIs)
protein-protein interactions (PPIs)
support vector machine (SVM)

Access to Document

10.1109/ICMLC.2012.6358969

Cite this

Chu, Y. S., Liu, Y. Q., & Wu, Q. (2012). SVM-based prediction of protein-protein interactions of Glucosinolate biosynthesis. In Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 (pp. 471-476). Article 6358969 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2012.6358969

@inproceedings{fe4d89c6953d4cb49774727126575075,

title = "SVM-based prediction of protein-protein interactions of Glucosinolate biosynthesis",

abstract = "Protein-protein interactions (PPIs) are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more PPIs of Glucosinolates biosynthetic pathways and removing PPIs falsely predicted. A support vector machine (SVM) predictor with the radial basis kernel function (RBF kernel) is trained based on the domain and domain-domain interaction (DDI) information of the amino acid sequences. In this paper, a symmetrical pair of feature vectors is used to represent the symmetrical relationship between two proteins, and 5-fold cross-validation is used to search the best SVM parameters. Then the best SVM parameters are used to train the SVM-based PPIs predictor. The proteins originate from gene AT4G14800 and ATSGS4810 (ID of Arabidopsis Genome Initiative (AGI)), ATSGOS730 and AT4G18040, ATlG04S10 and ATSGOS260 are affirmed interactive by this SVM-based PPIs predictor.",

keywords = "domain-domain interactions (DDIs), protein-protein interactions (PPIs), support vector machine (SVM)",

author = "Chu, \{Yan Shuo\} and Liu, \{Ya Qiu\} and Qu Wu",

year = "2012",

doi = "10.1109/ICMLC.2012.6358969",

language = "英语",

isbn = "9781467314855",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

publisher = "IEEE Computer Society",

pages = "471--476",

booktitle = "Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012",

address = "美国",

note = "2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 ; Conference date: 15-07-2012 Through 17-07-2012",

}

Chu, YS, Liu, YQ & Wu, Q 2012, SVM-based prediction of protein-protein interactions of Glucosinolate biosynthesis. in Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012., 6358969, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2, IEEE Computer Society, pp. 471-476, 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012, Xian, Shaanxi, China, 15/07/12. https://doi.org/10.1109/ICMLC.2012.6358969

SVM-based prediction of protein-protein interactions of Glucosinolate biosynthesis. / Chu, Yan Shuo; Liu, Ya Qiu; Wu, Qu.
Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. IEEE Computer Society, 2012. p. 471-476 6358969 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2).

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

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AU - Liu, Ya Qiu

AU - Wu, Qu

PY - 2012

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N2 - Protein-protein interactions (PPIs) are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more PPIs of Glucosinolates biosynthetic pathways and removing PPIs falsely predicted. A support vector machine (SVM) predictor with the radial basis kernel function (RBF kernel) is trained based on the domain and domain-domain interaction (DDI) information of the amino acid sequences. In this paper, a symmetrical pair of feature vectors is used to represent the symmetrical relationship between two proteins, and 5-fold cross-validation is used to search the best SVM parameters. Then the best SVM parameters are used to train the SVM-based PPIs predictor. The proteins originate from gene AT4G14800 and ATSGS4810 (ID of Arabidopsis Genome Initiative (AGI)), ATSGOS730 and AT4G18040, ATlG04S10 and ATSGOS260 are affirmed interactive by this SVM-based PPIs predictor.

AB - Protein-protein interactions (PPIs) are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more PPIs of Glucosinolates biosynthetic pathways and removing PPIs falsely predicted. A support vector machine (SVM) predictor with the radial basis kernel function (RBF kernel) is trained based on the domain and domain-domain interaction (DDI) information of the amino acid sequences. In this paper, a symmetrical pair of feature vectors is used to represent the symmetrical relationship between two proteins, and 5-fold cross-validation is used to search the best SVM parameters. Then the best SVM parameters are used to train the SVM-based PPIs predictor. The proteins originate from gene AT4G14800 and ATSGS4810 (ID of Arabidopsis Genome Initiative (AGI)), ATSGOS730 and AT4G18040, ATlG04S10 and ATSGOS260 are affirmed interactive by this SVM-based PPIs predictor.

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DO - 10.1109/ICMLC.2012.6358969

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ER -

SVM-based prediction of protein-protein interactions of Glucosinolate biosynthesis

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