Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …

Co-expression network analysis is one of the most powerful approaches for interpretation of
large transcriptomic datasets. It enables characterization of modules of co-expressed genes …

Gene co-expression networks can be used to associate genes of unknown function with
biological processes, to prioritize candidate disease genes or to discern transcriptional …

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 …

Introduction Many chronic disorders have genomic etiology, disease progression, clinical
presentation, and response to treatment that vary on a patient-to-patient basis. Such …

Introduction The authors of this work propose an unsupervised machine learning model that
has the ability to identify real-world latent infectious diseases by mining social media data. In …

Co-clustering aims to explore coherent patterns by simultaneously clustering samples and
features of data. Several co-clustering methods have been proposed in the past decades …

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 …

Background Elucidation of regulatory networks, including identification of regulatory
mechanisms specific to a given biological context, is a key aim in systems biology. This has …

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