Data shuffling and statistical analysis on microarray data for gene selection: a comparative study on filtering methods
by Zejin Ding, Yan-Qing Zhang, Yichuan Zhao
International Journal of Functional Informatics and Personalised Medicine (IJFIPM), Vol. 3, No. 3, 2010

Abstract: Computational analysis have been broadly used to discover disease-relevant genes from microarray expression data. In this paper, we extend a traditional statistical metric to a second level to measure gene-disease relations, testing such relation whether can be replicated by randomly shuffling the gene expression data. The traditional metric can be considered as a first-level metric; the relevance of each gene is then verified through the second-level significance testing based on the first-level metric calculated on the original data and shuffled data. We show that this method can also produce high classification performance, compared with other filter-based methods.

Online publication date: Thu, 17-Mar-2011

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 Functional Informatics and Personalised Medicine (IJFIPM):
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