Biclustering of Microarray Gene Expression Data

ISBN-13: 9783659746390 / Angielski / Miękka / 2015 / 56 str.

Kandasamy Premalatha;Annadasampalayam Mathaiyan Natarajan;Rengeswaran Balamurugan

Opis

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.

O autorze

R. 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 and Engineering) from Anna University, Chennai. His areas of interest including data mining and heuristic optimization techniques