Semi-supervised clustering algorithm for haplotype assembly problem based on MEC model
by Xin-Shun Xu; Ying-Xin Li
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 6, No. 4, 2012

Abstract: Haplotype assembly is to infer a pair of haplotypes from localized polymorphism data. In this paper, a semi-supervised clustering algorithm—SSK (Semi-Supervised K-means) is proposed for it, which, to our knowledge, is the first semi-supervised clustering method for it. In SSK, some positive information is firstly extracted. The information is then used to help k-means to cluster all SNP fragments into two sets from which two haplotypes can be reconstructed. The performance of SSK is tested on both real data and simulated data. The results show that it outperforms several state-of-the-art algorithms on Minimum Error Correction (MEC) model.

Online publication date: Wed, 17-Dec-2014

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