Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets

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Motivation: Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. Results: We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology.
Publisher
OXFORD UNIV PRESS
Issue Date
2010-06
Language
English
Article Type
Article
Keywords

RANDOM FORESTS CLASSIFICATION; WIDE EXPRESSION PROFILES; PROSTATE-CANCER; ANDROGEN RECEPTOR; BREAST-CANCER; DNA-REPLICATION; PATHWAY; NETWORKS; ENRICHMENT; CELLS

Citation

BIOINFORMATICS, v.26, no.12, pp.1506 - 1512

ISSN
1367-4803
DOI
10.1093/bioinformatics/btq207
URI
http://hdl.handle.net/10203/99742
Appears in Collection
BiS-Journal Papers(저널논문)
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