Module-based biomarker discovery in breast cancer

Yuji Zhang, Jason J. Xuan, Robert Clarke, Habtom W. Ressom

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The availability of genome-wide biological network data opens up new possibilities to discover novel biomarkers and elucidate cancer-related complex mechanisms at network level. In this paper, we propose a novel module-based feature selection framework, which integrates biological network information and gene expression data to identify biomarkers, not as individual genes but as functional modules. Also, a large-scale analysis of ensemble feature selection concept is presented. The method allows combining features selected from multiple runs with various data subsampling to increase the reliability and classification accuracy of the final set of selected features. The results from four breast cancer studies demonstrate that the identified module biomarkers achieve: i) higher classification accuracy in independent validation datasets; ii) better reproducibility than individual gene biomarkers; iii) improved biological interpretability; and iv) enhanced enrichment in cancer-related "disease drivers".

Original languageEnglish (US)
Title of host publicationProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Pages352-356
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China
Duration: Dec 18 2010Dec 21 2010

Publication series

NameProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

Conference

Conference2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
CountryChina
CityHong Kong
Period12/18/1012/21/10

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