Biclustering of Microarray Gene Expression Data :

Balamurugan Rengeswaran

ISBN 10: 3659746398 / ISBN 13: 9783659746390

Published by LAP Lambert Academic Publishing Jul 2015, 2015

About this Item

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

Bibliographic Details

• Title: Biclustering of Microarray Gene Expression Data :
• Publisher: LAP Lambert Academic Publishing Jul 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 3659746398
• ISBN 13: 9783659746390
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.