An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to t...

Binding of transcription factors to the promoter regions of genes can be measured genome-wide to reveal regulatory networks. The measurements are expensive, however, and methods for predicting bindings from earlier data would reduce the cost. At best, genome-wide studies could be targeted based on a few test samples. We model the binding patterns using recent ideas from collaborative filtering ...

Several problem in Artificial Intelligence and Pattern Recognition are computationally intractable due to their inherent complexity and the exponential size of the solution space. One example of such problems is biclustering, a specific clustering problem where rows and columns of a data-matrix must be clustered simultaneously. Quantum information processing could provide a viable alternative t...

Mrvar and Doreian recently defined a notion of bipartite clustering in bipartite signed graphs that gives a measure of imbalance of the signed graph, different from previous measures (the “frustration index” or “line index of balance”, l, and Davis’s clusterability). A biclustering of a bipartite signed graph is a pair (π1, π2) of partitions of the two color classes; the sets of the partitions ...

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 ide...

Analysts must filter through an ever-growing amount of data to obtain information relevant to their investigations. Looking at every piece of information individually is in many cases not feasible; there is hence a growing need for new filtering tools and techniques to improve the analyst process with large datasets. We present MineVis – an analytics system that integrates biclustering algorith...

We consider a generalized version of the correlation clustering problem, defined as follows. Given a complete graph G whose edges are labeled with + or −, we wish to partition the graph into clusters while trying to avoid errors: + edges between clusters or − edges within clusters. Classically, one seeks to minimize the total number of such errors. We introduce a new framework that allows the o...

Microarray technology is a powerful method for monitoring the expression level of thousands of genes in parallel. Using this technology, the expression levels of genes are measured. Microarray data is represented in N × M matrix. Each row indicates genes and each column indicates condition. In Gene Expression data, standard clustering algorithms are called as global clustering. In global cluste...