Network-constrained support vector machine for classification

Li Chen, Jianhua Xuan, Yue Wang, Rebecca B. Riggins, Robert Clarke

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

Abstract

One of the major goals in microarray data analysis is to identify biomarkers and build a classification model for future prediction. Many traditional statistical models, based on microarray data alone, often fail in identifying biologically meaningful genes, which should have synergistic effect on determine the clinical outcomes through some interactions rather than work individually. In this paper, we proposed a network-constrained support vector machine (nSVM) for classification by incorporating prior knowledge, which could be proteinprotein interactions, protein-gene regulation relationships or pathways information. Specifically, we use Laplacian matrix to represent gene-gene interaction network to regularize the objective function of SVM, which imposes the smoothness of coefficients over the network. The experimental results on simulation and real microarray datasets demonstrate that our method could not only improve classification performance compared to conventional SVM, but more importantly, it could identify significant sub-networks belonging to several pathways which might be related to underlying mechanism associated with clinical outcomes.

Original languageEnglish (US)
Title of host publicationProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Pages60-65
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States
Duration: Dec 11 2008Dec 13 2008

Publication series

NameProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Other

Other7th International Conference on Machine Learning and Applications, ICMLA 2008
CountryUnited States
CitySan Diego, CA
Period12/11/0812/13/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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