Robust Signature Discovery for Affymetrix GeneChip

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


Phenotype prediction is one of the central issues in genetics and medical sciences research. Due to the advent of highthroughput screening technologies, microarray-based cancer classification has become a standard procedure to identify cancer-related gene signatures. Since gene expression profiling in transcriptome is of high dimensionality, it is a challenging task to discover a biologically functional signature over different cell lines. In this article, we present an innovative framework for finding a small portion of discriminative genes for a specific disease phenotype classification by using information theory. The framework is a data-driven approach and considers feature relevance, redundancy, and interdependence in the context of feature pairs. Its effectiveness has been validated by using a brain cancer benchmark, where the gene expression profiling matrix is derived from Affymetrix Human Genome U95Av2 GeneChip®. Three multivariate filters based on information theory have also been used for comparison. To show the strengths of the framework, three performance measures, two sets of enrichment analysis, and a stability index have been used in our experiments. The results show that the framework is robust and able to discover a gene signature having a high level of classification performance and being more statistically significant enriched.
Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence
Subtitle of host publicationICAART 2014 Revised Selected Papers
EditorsBéatrice Duval, Jaap van den Herik, Stephane Loiseau, Joaquim Filipe
Number of pages17
ISBN (Electronic)9783319252100
ISBN (Print)9783319252094
Publication statusPublished - Oct 2015


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