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Biclustering is an unsupervised classification technique that plays an increasingly important role in the study of modern biology. This data mining technique has provided answers to several challenges raised by the analysis of biological data and more particularly the analysis of gene expression data.
In this study, we will describe some recent literature on biclustering as well as a multi-objective evolutionary biclustering framework for gene expression data ...
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Abstract. With the advent of microarray technology it has been possible to measure thousands of expression values of genes in a single experiment.
Feb 23, 2013 · Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions ...
In this study, a novel multi-objective evolutionary biclustering framework is introduced by incorporating local search strategies.
EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets.
This paper proposes a new evolutionary approach to obtain maximal high-quality biclusters of highly-correlated genes. The performance of the proposed algorithm ...
Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based ...
This paper proposes a new evolutionary approach to obtain maximal high-quality biclusters of highly-correlated genes. The performance of the proposed algorithm ...
In this study, a novel evolutionary framework is introduced for generating optimal fuzzy possibilistic biclusters from microarray gene expression data. The ...