Robust signature discovery for affymetrix GeneChip® cancer classification

Lai, Hung-Ming, Albrecht, Andreas A. and Steinhofel, Kathleen (2015) Robust signature discovery for affymetrix GeneChip® cancer classification. Agents and Artificial Intelligence: 6th International Conference, ICAART 2014, Angers, France, March 6-8, 2014, Revised Selected Papers. In: 6th International Conference on Agents and Artificial Intelligence, 6-8 Mar 2014, Angers, France. ISBN 9783319252094. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-319-25210-0_20)

Abstract

Phenotype prediction is one of the central issues in genetics and medical sciences research. Due to the advent of high-throughput 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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: Agents and Artificial Intelligence, Volume 8946 of the series Lecture Notes in Computer Science pp 329-345
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 17712
Useful Links:
Depositing User: Andreas Albrecht
Date Deposited: 29 Sep 2015 08:58
Last Modified: 12 Jun 2019 12:49
URI: https://eprints.mdx.ac.uk/id/eprint/17712

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