Driven by high-throughput sequencing techniques, modern genomic and clinical studies are
in a strong need of integrative machine learning models for better use of vast volumes of …

Biclustering is an unsupervised machine-learning technique that simultaneously clusters
rows and columns in a data matrix. Over the past two decades, the field of biclustering has …

Fecal microbiome variation in the average, healthy population has remained under-
investigated. Here, we analyzed two independent, extensively phenotyped cohorts: the …

A critical step in the analysis of large genome-wide gene expression datasets is the use of
module detection methods to group genes into co-expression modules. Because of …

The need to analyze high-dimension biological data is driving the development of new data
mining methods. Biclustering algorithms have been successfully applied to gene expression …

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 …

DNA microarray technologies are used extensively to profile the expression levels of
thousands of genes under various conditions, yielding extremely large data-matrices. Thus …

Background The effectiveness of biclustering, simultaneous clustering of rows and columns
in a data matrix, was shown in gene expression data analysis. Several researchers …

Biclustering is a powerful data mining technique that allows clustering of rows and columns,
simultaneously, in a matrix-format data set. It was first applied to gene expression data in …

Motivation The biclustering of large-scale gene expression data holds promising potential
for detecting condition-specific functional gene modules (ie biclusters). However, existing …