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 …

Network inference, which is the reconstruction of biological networks from high-throughput
data, can provide valuable information about the regulation of gene expression in cells …

As a prolific research area in data mining, subspace clustering and related problems
induced a vast quantity of proposed solutions. However, many publications compare a new …

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 grouping genes …

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

Next-generation DNA sequencing technologies are enabling genome-wide measurements
of somatic mutations in large numbers of cancer patients. A major challenge in the …

This survey provides a description of algorithms to reorder visual matrices of tabular data
and adjacency matrix of Networks. The goal of this survey is to provide a comprehensive list …

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

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It
is emerging as a standard tool for extracting knowledge from gene expression …

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 …