A new computational framework for gene expression clustering

Authors:
Shahreen Kasim;Safaai Deris;Razib M. Othman
Affiliations:
Dept. of Inf. System, Fac. of Inf. Techn. and Multimedia, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia and Artificial Int. and Artificial Int. and Bioinf. Res. Group, Fac. of Comp. Sc ...;Laboratory of Computational Intelligence and Biotechnology, Universiti Teknologi Malaysia, Skudai, Malaysia;Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science, and Information Systems Universiti Teknologi Malaysia, Skudai, Malaysia
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

Citing 2
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BicAT: a biclustering analysis toolbox

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Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data

Computers in Biology and Medicine

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Abstract

Clustering of gene expression is a useful exploratory technique for gene expression dataset as it groups similar objects together and identify potentially meaningful relationships between the objects. However, there are several issues arise for instance data intensive and redundancy in the cluster. Therefore, the new computational framework is needed in order to handle these issues. The results showed that the proposed computational framework achieved better results compared with other methods.