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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yuji Zhang Xuan, J.J. Clarke, R. Ressom, H.W. |
| Copyright Year | 2010 |
| Description | Author affiliation: Dept. of Electrical & Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA (Yuji Zhang; Xuan, J.J.) || Lombardi Comprehensive Cancer Center, Georgetown University medical Center, Washington, DC (Clarke, R.; Ressom, H.W.) |
| 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”. |
| Starting Page | 352 |
| Ending Page | 356 |
| File Size | 367452 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424483068 |
| e-ISBN | 9781424483075 |
| DOI | 10.1109/BIBM.2010.5706590 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Breast cancer Gene expression Bioinformatics Diseases Driver circuits |
| Content Type | Text |
| Resource Type | Article |
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