NDLI: Clustering of SNPs by a Structural EM Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Yulong Zhang Liang Ji
Copyright Year	2009
Abstract	In population based human genetic studies, unrelated individuals are collected and SNPs are measured. There are several kinds of generative models proposed for modeling the data containing a large number of SNPs loci according to the characters of human genome. However, such models can only deal with ordered loci. In this paper, we try to model the same data without using the order information. Firstly, we present a clustering model for SNPs by modifying the multi-block model used in GERBIL. It is a two-layer Bayesian network with multiple latent variables. It does not use the order information of the loci. Secondly, we solve the model by employing a structural EM algorithm combined with simulated annealing mechanism. A real data set was analyzed by the model. The results show that the SNPs can be clustered effectively. Such a model is potentially useful for clustering distantly correlated SNPs loci.
Starting Page	147
Ending Page	150
File Size	1231581
Page Count	4
File Format	PDF
ISBN	9780769537399
DOI	10.1109/IJCBS.2009.97
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sequences Data analysis generative mdoel simulated annealing Humans Genomics Bayesian methods Clustering algorithms Hidden Markov models Simulated annealing Genetics block Bioinformatics Bayesian network EM algorithm latent variable
Content Type	Text
Resource Type	Article

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