Biclustering has become a popular technique for the study of gene expression data,
especially for discovering functionally related gene sets under different subsets of …

Mouse-tracking data, which record computer mouse trajectories while participants perform
an experimental task, provide valuable insights into subjects' underlying cognitive …

Although several biclustering algorithms have been studied, few are used for cross-pattern
identification across species using multi-omics data mining. A fast empirical Bayesian …

Supplement to “The Gibbs-plaid biclustering model”. A high-resolution version of the image
shown in Figure 6, as well as the complete biclustering results associated with the RD data …

Many biclustering algorithms and bicluster criteria have been proposed in analyzing the
gene expression data. However, there are no clues about the choice of a specific …

Motivated by classes of problems frequently found in the analysis of gene expression data,
we propose a semiparametric Bayesian model to detect biclusters, that is, subsets of …

We introduce a Bayesian approach for biclustering that accounts for the prior functional
dependence between genes using hidden Markov models (HMMs). We utilize biological …

In gene expression data analysis, biclustering has proven to be an effective method of
finding local patterns among subsets of genes and conditions. The task of evaluating the …

Analysis of gene expression microarray data interprets the actual expression data for
revealing relevant information regarding genes, proteins, diseases etc. DNA microarrays …

Gene expression levels of organisms are measured by DNA microarrays. Finding biclusters
in gene expression matrices provides invaluable information about effects of disease at the …