Abstract Biclustering is a two-dimensional data analysis technique that, applied to matrix, searches for subset of rows and columns that intersect produce submatrix with given, expected features. Such an approach requires different methods those typical classification or regression tasks. In recent years it has become possible express biclustering goals in the form Boolean reasoning. This paper...

In the course we have already seen different Biclustering methods such as Cheng-Church, ISA, SAMBA (see scribe 5), OPSM (see scribe 9). The method described in this lecture is Bimax an algorithm due to Prelić et al. [2]. It uses a simple data model reflecting the fundamental idea of biclustering, while aiming to determine all optimal biclusters in reasonable time. This method has the benefit of...

MOTIVATION In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness ha...

BACKGROUND Biclustering is a powerful technique for identification of co-expressed gene groups under any (unspecified) substantial subset of given experimental conditions, which can be used for elucidation of transcriptionally co-regulated genes. RESULTS We have previously developed a biclustering algorithm, QUBIC, which can solve more general biclustering problems than previous biclustering ...

Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the genera...

Multiple Structure Recovery (MSR) represents an important and challenging problem in the field of Computer Vision and Pattern Recognition. Recent approaches to MSR advocate the use of clustering techniques. In this paper we propose an alternative method which investigates the usage of biclustering in MSR scenario. The main idea behind the use of biclustering approaches to MSR is to isolate subs...

Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNAmicroarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of bicluste...

Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the se...

Mining matrices to find relevant biclusters, subsets of rows exhibiting a coherent pattern over a subset of columns, is a critical task for a wide-set of biomedical and social applications. Since biclustering is a challenging combinatorial optimization task, existing approaches place restrictions on the allowed structure, coherence and quality of biclusters. Biclustering approaches relying on p...

Biclustering is a methodology allowing simultaneous partitioning of a set of samples and their features into classes. Samples and features classified together are supposed to have a high relevance to each other which can be observed by intensity of their expressions. The notion of consistency for biclustering is defined using interrelation between centroids of sample and feature classes. Consis...