iRDA: a new filter towards predictive, stable, and enriched candidate genes

Lai, Hung-Ming, Albrecht, Andreas A. and Steinhofel, Kathleen (2015) iRDA: a new filter towards predictive, stable, and enriched candidate genes. BMC Genomics, 16 . ISSN 1471-2164 (doi:10.1186/s12864-015-2129-5)

Abstract

Background: Gene expression profiling using high-throughput screening (HTS) technologies allows clinical researchers to find prognosis gene signatures that could better discriminate between different phenotypes and serve as potential biological markers in disease diagnoses. In recent years, many feature selection methods have been devised for finding such discriminative genes, and more recently information theoretic filters have also been introduced for capturing feature-to-class relevance and feature-to-feature correlations in microarray-based classification.
Methods: In this paper, we present and fully formulate a new multivariate filter, iRDA, for the discovery of HTS gene-expression candidate genes. The filter constitutes a four-step framework and includes feature relevance, feature redundancy, and feature interdependence in the context of feature-pairs. The method is based upon approximate Markov blankets, information theory, several heuristic search strategies with forward, backward and insertion phases, and the method is aiming at higher order gene interactions.
Results: To show the strengths of iRDA, three performance measures, two evaluation schemes, two stability index sets, and the gene set enrichment analysis (GSEA) are all employed in our experimental studies. Its effectiveness has been validated by using seven well-known cancer gene-expression benchmarks and four other disease experiments, including a comparison to three popular information theoretic filters. In terms of classification performance, candidate genes selected by iRDA perform better than the sets discovered by the other three filters. Two stability measures indicate that iRDA is the most robust with the least variance. GSEA shows that iRDA produces more statistically enriched gene sets on five out of the six benchmark datasets.
Conclusions: Through the classification performance, the stability performance, and the enrichment analysis, iRDA is a promising filter to find predictive, stable, and enriched gene-expression candidate genes.

Item Type: Article
Additional Information: Article number: 1041
Keywords (uncontrolled): Cancer phenotype prediction; Feature selection and classification; Microarray; Prognosis gene signature; Transcriptomic profiling
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 18553
Depositing User: Andreas Albrecht
Date Deposited: 10 Dec 2015 10:19
Last Modified: 12 Jun 2019 12:24
URI: https://eprints.mdx.ac.uk/id/eprint/18553

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