MOTIVATION During the last years, the discovering of biclusters in data is becoming more and more popular. Biclustering aims at extracting a set of clusters, each of which might use a different subset of attributes. Therefore, it is clear that the usefulness of biclustering techniques is beyond the traditional clustering techniques, especially when datasets present high or very high dimensional...

The small sample sizes and high dimensionality of gene expression datasets pose significant problems for unsupervised subgroup discovery. While the stability of unidimensional clustering algorithms has been previously addressed, generalizing existing approaches to biclustering has proved extremely difficult. Despite these difficulties, developing a stable biclustering algorithm is essential for...

Biclustering algorithms have emerged as an important tool for the discovery of local patterns in gene expression data. For the case where the expression data corresponds to time-series, efficient algorithms that work with a discretized version of the expression matrix are known. However, these algorithms assume that the biclusters to be found are perfect, in the sense that each gene in the bicl...

Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...

Biclustering techniques have been successfully applied to analyze microarray data and they begin to be applied to the analysis of mass spectrometry data, a high-throughput technology for proteomic data analysis which has been an active research area during the last years. In this work, we propose a novel workflow to the application of biclustering to MALDI-TOF mass spectrometry data, supported ...

Biclustering, namely simultaneous clustering of genes and samples, represents a challenging and important research line in the expression microarray data analysis. In this paper, we investigate the use of Affinity Propagation, a popular clustering method, to perform biclustering. Specifically, we cast Affinity Propagation into the Couple Two Way Clustering scheme, which allows to use a clusteri...

Biclustering is a simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets (clusters). Samples and features clustered together are supposed to have a high relevance to each other. In this paper we provide a new mathematical programming formulation for unsupervised biclustering. The proposed model involves the solution of a fractional 0-1 programmin...

Consistent biclusterings of sets of data are useful for solving feature selection and classification problems. The problem of finding a consistent biclustering can be formulated as a combinatorial optimization problem, and it can be solved by the employment of a recently proposed VNS-based heuristic. In this context, the concept of β-consistent biclustering has been introduced for dealing with ...

Conventional clustering technique for gene expression data provides a global view of the data. In the biological prospective, a local view is essential for better analysis of gene expression data with simultaneous grouping of genes and conditions. Several biclustering techniques have been proposed in the literature based on different problem formulation. Therefore, it is difficult to compare th...