Semi-supervised clustering algorithm for haplotype assembly problem based on MEC model Online publication date: Wed, 17-Dec-2014
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 algorithmSSK (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.
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