Efficient Super Granular SVM Feature Elimination (Super GSVM-FE) model for protein sequence motif information extraction
by Bernard Chen, Stephen Pellicer, Phang C. Tai, Robert Harrison, Yi Pan
International Journal of Functional Informatics and Personalised Medicine (IJFIPM), Vol. 1, No. 1, 2008

Abstract: Protein sequence motifs are gathering progressively attention in the sequence analysis area. The conserved regions have the potential to determine the conformation, function and activities of the proteins. We develop a new method combines the concept of granular computing and the power of Ranking-SVM to further extract protein sequence motif information generated from the FGK model. The quality of motif information increases dramatically in all three evaluation measures by applying this new feature elimination model. Since the training step of Ranking SVM is very time consuming, we provide a feasible way to reduce the training time dramatically without sacrificing the quality.

Online publication date: Wed, 14-May-2008

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