Methodology article

Classification and biomarker identification using gene network modules and support vector machines

Malik Yousef^1,2 , Mohamed Ketany³ , Larry Manevitz³ , Louise C Showe⁴ and Michael K Showe⁴

¹  The Institute of Applied Research - The Galilee Society, Shefa-Amr, Israel

²  Al-Qasemi Academic College, Baqa Algharbiya, Israel

³  Computer Science Department, University of Haifa, Haifa, Israel

⁴  Molecular Oncogenesis/Systems Biology Program, The Wistar Institute, Philadelphia, PA 19104, USA

author email corresponding author email

BMC Bioinformatics 2009, 10:337doi:10.1186/1471-2105-10-337

Published:	15 October 2009

Abstract

Background

Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.

We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)

Results

Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form n clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.

Conclusion

More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.

The Matlab version of SVM-RNE can be downloaded from http://web.macam.ac.il/~myousef webcite