Module-based biomarker discovery in breast cancer

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

doi:10.1109/BIBM.2010.5706590

Module-based biomarker discovery in breast cancer

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Pages	352-356
Number of pages	5
DOIs	https://doi.org/10.1109/BIBM.2010.5706590
State	Published - 2010
Externally published	Yes
Event	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China Duration: Dec 18 2010 → Dec 21 2010

Publication series

Name	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

Conference

Conference	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Country/Territory	China
City	Hong Kong
Period	12/18/10 → 12/21/10

Bibliographical note

Funding Information:
* Supported by Grant 6287 from the Austrian National Bank. by Grant P8947Med from the Austrian Science Foundation and by the Pro- vince of Tyrol. t Address for correspondence: Richard Greil, M.D. Laboratory of Molecular Cytology, Department of Internal Medicine, University of Innsbruck, Anichstrasse 35, A-6020 Innsbruck, Austria, Tel.: 0512 504 3343, Fax: 0512 504 3343.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/BIBM.2010.5706590

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Zhang, Y., Xuan, J. J., Clarke, R., & Ressom, H. W. (2010). Module-based biomarker discovery in breast cancer. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 (pp. 352-356). [5706590] (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010). https://doi.org/10.1109/BIBM.2010.5706590

Module-based biomarker discovery in breast cancer. / Zhang, Yuji; Xuan, Jason J.; Clarke, Robert et al.

Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. p. 352-356 5706590 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, Y, Xuan, JJ, Clarke, R & Ressom, HW 2010, Module-based biomarker discovery in breast cancer. in Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010., 5706590, Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, pp. 352-356, 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, Hong Kong, China, 12/18/10. https://doi.org/10.1109/BIBM.2010.5706590

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note = "Funding Information: * Supported by Grant 6287 from the Austrian National Bank. by Grant P8947Med from the Austrian Science Foundation and by the Pro- vince of Tyrol. t Address for correspondence: Richard Greil, M.D. Laboratory of Molecular Cytology, Department of Internal Medicine, University of Innsbruck, Anichstrasse 35, A-6020 Innsbruck, Austria, Tel.: 0512 504 3343, Fax: 0512 504 3343.; 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 ; Conference date: 18-12-2010 Through 21-12-2010",

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N2 - 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".

AB - 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".

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Module-based biomarker discovery in breast cancer

Abstract

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Bibliographical note

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