The biclustering of microarray data has been the subject of a large research. No one of the existing biclustering algorithms is perfect. The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work. Biological validation of biclusters of microarray data is one of the most important open issues. So far, there are no general guidelines in the literature on how to validate biologically extracted biclusters. In this paper, we develop two biclustering algorithms of binary microarray data, adopting the Iterative Row and Column Clustering Combination (IRCCC) approach, called BiBinCons and BiBinAlter. However, the BiBinAlter algorithm is an improvement of BiBinCons. On the other hand, BiBinAlter differs from BiBinCons by the use of the EvalStab and IndHomog evaluation functions in addition to the CroBin one (Bioinformatics 20:1993–2003, 2004). BiBinAlter can extracts biclusters of good quality with better p-values.

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.

Abstract-An 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.

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