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Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data.In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced.
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Biclustering of Microarray Gene Expression Data :
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LAP Lambert Academic Publishing Jul 2015, 2015
ISBN 10: 3659746398
ISBN 13: 9783659746390
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data.In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced. 56 pp. Englisch. Seller Inventory # 9783659746390
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Biclustering of Microarray Gene Expression Data : : With Heuristic Approach
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LAP Lambert Academic Publishing, 2015
ISBN 10: 3659746398
ISBN 13: 9783659746390
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data.In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced. Seller Inventory # 9783659746390
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Biclustering of Microarray Gene Expression Data :
Published by
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659746398
ISBN 13: 9783659746390
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Seller: moluna, Greven, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rengeswaran BalamuruganR. Balamurugan is currently working as a Senior Research Fellow for the DBT sponsored project at Bannari Amman Institute of Technology, Erode, Tamil Nadu, India. He received his M.E.and B.E. (Computer Science a. Seller Inventory # 158429065
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Biclustering of Microarray Gene Expression Data :
Published by
LAP Lambert Academic Publishing Jul 2015, 2015
ISBN 10: 3659746398
ISBN 13: 9783659746390
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. Neuware -Extracting meaningful information from gene expression data poses a great challenge to the community of researchers in the field of computation as well as to biologists. It is possible to determine the behavioral patterns of genes such as nature of their interaction, similarity of their behavior and so on, through the analysis of gene expression data.In order to identify various patterns from gene expression data, data mining techniques are essential. Major data mining techniques which can be applied for the analysis of gene expression data include clustering, classification, association rule mining etc. Clustering is an important data mining technique for the analysis of gene expression data. However clustering has some disadvantages. To overcome the problems associated with clustering, biclustering is introduced.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch. Seller Inventory # 9783659746390
Quantity: 2 available