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 …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

DNA microarray technology has now made it possible to simultaneously monitor the
expression levels of thousands of genes during important biological processes and across …

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

Although less than a decade old, the field of microarray data analysis is now thriving and
growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as …

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 …

In this paper we introduce a novel algorithm called TRICLUSTER, for mining coherent
clusters in three-dimensional (3D) gene expression datasets. TRICLUSTER can mine …

Motivation Increasing number of gene expression profiles has enabled the use of complex
models, such as deep unsupervised neural networks, to extract a latent space from these …

The enormous complexity of biological systems at the molecular level must be answered
with powerful computational methods. Computational biology is a young field, but has seen …