Protein model assessment via machine learning techniques
by Anjum Reyaz-Ahmed, Robert Harrison, Yan-Qing Zhang
International Journal of Functional Informatics and Personalised Medicine (IJFIPM), Vol. 3, No. 3, 2010

Abstract: We attempt to solve the problem of protein model assessment using machine learning techniques and information from sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and given a new model, predicts whether or not it belongs to the class of PDB structures. We show two such machines (SVM and FDT); results appear promising for further analysis. To reduce computational overhead, multiprocessor environment and basic feature selection method is used. The prediction accuracy using improved FDT is above 80% and results are better when compared with other machine learning techniques.

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