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

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved
its effectiveness in bioinformatics due to its capacity to produce local instead of global …

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

High dimensional and small samples characterize gene expression data and contain a large
number of genes unrelated to disease. Feature selection improves the efficiency of disease …

Performing clustering analysis is one of the important research topics in cancer discovery
using gene expression profiles, which is crucial in facilitating the successful diagnosis and …

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 …

To understand how subspace clustering algorithms discover distinct bicluster types and how
their effectiveness has been validated, we offer a systematic literature review on available …

A novel classification method using ℓ2, 1-norm based regression is proposed in this paper.
The ℓ2, 1-norm based loss function is robust to outliers or large variations distributed in the …

Tumor clustering is one of the important techniques for tumor discovery from cancer gene
expression profiles, which is useful for the diagnosis and treatment of cancer. While different …

An noticeable number of biclustering approaches have been proposed proposed for the
study of gene expression data, especially for discovering functionally related gene sets …