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Author Banka, Haider ♦ Mitra, Sushmita
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format HTM / HTML
Language English
Subject Keyword Knowledge discovery ♦ Multi-objective optimization ♦ Clustering ♦ Gene expression ♦ Genetic algorithms
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.
Description Affiliation: Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata (Banka, Haider) || Machine Intelligence Unit, Indian Statistical Institute, Kolkata (Mitra, Sushmita)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-06-29
Publisher Place New York
Journal Ubiquity (UBIQ)
Volume Number 2006
Issue Number October
Page Count 12
Starting Page 1
Ending Page 12


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Source: ACM Digital Library