Document Detail


Clustering of High Throughput Gene Expression Data.
MedLine Citation:
PMID:  23144527     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community.
Authors:
Harun Pirim; Burak Ekşioğlu; Andy Perkins; Cetin Yüceer
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Computers & operations research     Volume:  39     ISSN:  1873-765X     ISO Abbreviation:  Comput Oper Res     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101590913     Medline TA:  Comput Oper Res     Country:  -    
Other Details:
Languages:  ENG     Pagination:  3046-3061     Citation Subset:  -    
Affiliation:
Department of Industrial and Systems Engineering, Mississippi State University, P.O. Box 9542, Mississippi State, MS 39762.
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