Discovering maximal size coherent biclusters from gene expression data
by J. Bagyamani, K. Thangavel
International Journal of Healthcare Technology and Management (IJHTM), Vol. 12, No. 5/6, 2011

Abstract: Microarray experiments produce enormous amounts of data, leading to new requirements and challenges in bioinformatics. One of the major challenges in the analysis of such data sets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated with different physiological states. This proposed algorithm includes gene selection and extraction of biclusters from gene expression data using difference matrix. This improved algorithm extracts biclusters with maximum volume that may be left unidentified.

Online publication date: Sat, 28-Mar-2015

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 Healthcare Technology and Management (IJHTM):
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