A large number of clustering approaches have been proposed for the analysis of gene
expression data obtained from microarray experiments. However, the results from the …

Gene expression data hide vital information required to understand the biological process
that takes place in a particular organism in relation to its environment. Deciphering the …

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 grou** genes …

Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory
interactions, and apply the method to predict a large portion of the regulatory network of the …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

Biclustering consists in simultaneous partitioning of the set of samples and the set of their
attributes (features) into subsets (classes). Samples and features classified together are …

Bioinformatics research is characterized by voluminous and incremental datasets and
complex data analytics methods. The machine learning methods used in bioinformatics are …

Many clustering techniques have been proposed for the analysis of gene expression data
obtained from microarray experiments. However, choice of suitable method (s) for a given …

Biclustering or simultaneous clustering of both genes and conditions have generated
considerable interest over the past few decades, particularly related to the analysis of high …