Module-based breast cancer classification
by Yuji Zhang; Jianhua Xuan; Robert Clarke; Habtom W. Ressom
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 7, No. 3, 2013

Abstract: The reliability and reproducibility of gene biomarkers for classification of cancer patients has been challenged due to measurement noise and biological heterogeneity among patients. 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. Results from four breast cancer studies demonstrate that the identified module biomarkers 1) achieve higher classification accuracy in independent validation datasets; 2) are more reproducible than individual gene markers; 3) improve the biological interpretability of results; 4) are enriched in cancer 'disease drivers'.

Online publication date: Fri, 07-Jun-2013

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com