|Author||Banka, Haider ♦ Mitra, Sushmita|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|File Format||HTM / HTML|
|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|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
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