Non-negative Matrix Factorization (NMF), a classical method for dimensionality reduction,
has been applied in many fields. It is based on the idea that negative numbers are physically …

Cancer is a deadly disease which requires a very complex and costly treatment. Microarray
data classification plays an important role in cancer treatment. An efficient gene selection …

The extraction of predictive features from the complex high-dimensional multi-omic data is
necessary for decoding and overcoming the therapeutic responses in systems …

This paper introduces a distributed density based clustering approach that benefits from the
hesitant fuzzy weighted correlation coefficient as its similarity measure. All the proposed …

Many methods have been considered for gene selection and analysis of gene expression
data. Nonetheless, there still exists the considerable space for improving the explicitness …

This paper presents an exhaustive empirical study to identify biomarkers using two
approaches: frequency-based and network-based, over 17 different biclustering algorithms …

Biclustering of gene expression data aims at finding localized patterns in a subspace. A
bicluster (sometimes called a co-cluster), in the context of gene expression data, is a set of …

Although several biclustering algorithms have been studied, few are used for cross-pattern
identification across species using multi-omics data mining. A fast empirical Bayesian …

Biclusters hold significant importance in microarray analysis. Given the EA algorithm's
efficacy in tackling nonlinear problems, it has become a prevalent choice for evolutionary …

Digging up the coregulated gene biclusters using a novel Nature-inspired Meta-Heuristic
algorithm named Venus Flytrap Optimization (VFO). This optimized biclustering approach …