DNA Microarray Data Analysis: A New Survey on Biclustering

There are subsets of genes that have similar behavior under subsets of conditions, so we say that they coexpress, but behave independently under other subsets of conditions. Discovering such coexpressions can be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes that are coexpressed under clusters of conditions. This type of clustering is called biclustering.Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate this problem by finding suboptimal solutions. In this paper, we make a new survey on biclustering of gene expression data, also called microarray data

Block Mixture Model for the Biclustering of Microarray Data

This publication is a representation of what appears in the IEEE Digital Libraries.International audienceAn attractive way to make biclustering of genes and conditions is to adopt a Block Mixture Model (BMM). Approaches based on a BMM operate thanks to a Block Expectation Maximization (BEM) algorithm and/or a Block Classification Expectation Maximization (BCEM) one. The drawback of these approaches is their difficulty to choose a good strategy of initialization of the BEM and BCEM algorithms. This paper introduces existing biclustering approaches adopting a BMM and suggests a new fuzzy biclustering one. Our approach enables to choose a good strategy of initialization of the BEM and BCEM algorithms