An Improved Position Weight Matrix method based on an entropy measure for the recognition of prokaryotic promoters
by Qinqin Wu, Jiajun Wang, Hong Yan
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 5, No. 1, 2011

Abstract: In this paper, an Improved Position Weight Matrix (IPWM) method is proposed based on an entropy measure for the recognition of prokaryotic promoters. In this method, the conservative sites of the prokaryotic promoters are extracted according to an entropy measure, and then two improved position weight matrices are constructed based on the training set. By using the values of the matrix elements in the specific columns corresponding to the extracted conservative sites, the test sequences are scored and subsequently classified. Experiment results on several datasets show that the proposed algorithm outperforms the existing ones.

Online publication date: Sat, 24-Jan-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 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