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Cancer classification through high-throughput gene expression profiles has been widely used in biomedical research. Most recently, we portrayed a multivariate method for large scale gene selection based on information theorem with a central issue of feature interdependence and validated its effectiveness using a colon cancer benchmark. The completed research work now contributes to this article. Firstly, we have refined the method and proposed a complete framework to select a gene signature for a certain disease phenotype prediction under high-throughput technologies. The framework has then been applied to a brain cancer gene expression profiles derived from Affymetrix Human Genome U95Av2 Array, where the interrogated genes are six times more than that in the previous studied colon cancer data set. Three information theorem based filters were used for comparison. Our experimental result shows that the framework outperformed them in terms of classification performance with three performance measures. Additionally, to demonstrate how effectively feature interdependence has been tackled in the framework, two sets of enrichment analysis have also been performed. The results also show that more statistically significant gene sets and regulatory interactions could be found in our gene signature. Therefore, this framework could be promising for high-throughput gene selection around gene synergy.

A Framework for High-throughput Gene Signatures with Microarray-based Brain Cancer Gene Expression Profiling Data