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BMC Bioinformatics

, 10:337

First Online: 15 October 2009Received: 13 March 2009Accepted: 15 October 2009

Abstract

BackgroundClassification 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

ResultsInitially, 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.

ConclusionMore 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

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-10-337 contains supplementary material, which is available to authorized users.

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Author: Malik Yousef - Mohamed Ketany - Larry Manevitz - Louise C Showe - Michael K Showe

Source: https://link.springer.com/







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