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An Improved Unsupervised Phenotypes Structure Discovery Algorithm for Gene Expression Data
ZHAO Yuhai 1 * #,LI Yuan 2
1.Institute of Computer System, Northeastern University
2.Institute of Computer System, Northeastern University, Shenyang 110819
*Correspondence author
#Submitted by
Subject:
Funding: National Natural Science Foundation of China (No.No. 60803026), Ph.D. Programs Foundation (No.Young Teacher) of Ministry of Education of China)
Opened online:13 February 2011
Accepted by: none
Citation: ZHAO Yuhai,LI Yuan.An Improved Unsupervised Phenotypes Structure Discovery Algorithm for Gene Expression Data[OL]. [13 February 2011] http://en.paper.edu.cn/en_releasepaper/content/4408235
 
 
Phenotype structure discovery is a significant problem in bioinformatics. However, it receives less attension from an unsupervise learning perspective. This paper proposes an unsupervised phenotype structure discovery algorithm, namely UPID, to simultaneously mine phenotypes and informative genes from gene expression data. By adopting incremental computing optimization strategies, the calculation of UPID is greatly reduced. Furthermore, UPID decreases the impact of outliers by taking the sample proportion of each group into consideration, which makes the model more robust. Experiments conducted on several real gene expression datasets shows that UPID outperforms HS, a previous pattern detection method for gene expression data.
Keywords:Data mining; Phenotype; Informative genes; gene expression data
 
 
 

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