In:
Ubiquity, Association for Computing Machinery (ACM), Vol. 2006, No. October ( 2006-10), p. 1-12
Abstract:
With the advent of microarray technology it has been possible to measure thousands of expression values of genes in a single experiment. Biclustering or simultaneous clustering of both genes and conditions is challenging particularly for the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. The objective here is to find sub-matrices, i.e., maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a range of conditions while maximizing the volume simultaneously. Since these two objectives are mutually conflicting, they become suitable candidates for multi-objective modeling. In this study, we will describe some recent literature on biclustering as well as a multi-objective evolutionary biclustering framework for gene expression data along with the experimental results.
Type of Medium:
Online Resource
ISSN:
1530-2180
DOI:
10.1145/1183081.1183082
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2006
detail.hit.zdb_id:
2019525-4
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