Background: Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Although several biclustering algorithms have been studied, few are based on rigorous statistical models. Results: We developed a Bayesia...

In this paper, we describe an algorithm FARDiff (Fuzzy Adaptive Resonance Diffusion) which combines Diffusion Maps and Fuzzy Adaptive Resonance Theory to do clustering and biclustering on high dimensional data. We describe some applications of this method.

Several non-supervised machine learning methods have been used in the analysis of gene expression data obtained from microarray experiments. Recently, biclustering, a non-supervised approach that performs simultaneous clustering on the row and column dimensions of the data matrix, has been shown to be remarkably effective in a variety of applications. The goal of biclustering is to find subgrou...

Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue when classical clustering algorithms proved not to be good enough to detect similar expressions of genes under subset of conditions. Biclustering algorithms may be also applied to different datasets, such as medical, economical, social networks etc. In this article we explain the concept beneath h...

Biclustering is used for simultaneous clustering of the observations and variables when there no group structure known a priori. It being increasingly in bioinformatics, text analytics, so on. Previously, biclustering has been introduced model-based framework by utilizing similar to mixture factor analyzers. In such models, observed are modeled using latent variable that assumed be from . Clust...