Document Detail


Selection of Interdependent Genes via Dynamic Relevance Analysis for Cancer Diagnosis.
MedLine Citation:
PMID:  23124059     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Microarray analysis is widely accepted for human cancer diagnosis and classification. However the high dimensionality of microarray data poses a great challenge to classification. Gene selection plays a key role in identifying salient genes from thousands of genes in microarray data that can directly contribute to the symptom of disease. Although various excellent selection methods are currently available, one common problem of these methods is that genes which have strong discriminatory power as a group but are weak as individuals will be discarded. In this paper, a new gene selection method is proposed for cancer diagnosis and classification by retaining useful intrinsic groups of interdependent genes. The primary characteristic of this method is that the relevance between each gene and target will be dynamically updated when a new gene is selected. The effectiveness of our method is validated by experiments on six publicly available microarray data sets. Experimental results show that the classification performance and enrichment score achieved by our proposed method is better than those of other selection methods.
Authors:
Xin Sun; Yanheng Liu; Da Wei; Mantao Xu; Huiling Chen; Jiawei Han
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-1
Journal Detail:
Title:  Journal of biomedical informatics     Volume:  -     ISSN:  1532-0480     ISO Abbreviation:  J Biomed Inform     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-5     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100970413     Medline TA:  J Biomed Inform     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012. Published by Elsevier Inc.
Affiliation:
College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; School of Computing, University of Eastern Finland, Joensuu, FIN-80101, Finland. Electronic address: sunxin1984@yahoo.com.cn.
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