In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many
methods have been introduced for their statistical inference from gene expression data …

One of the most interesting, difficult, and potentially useful topics in computational biology is
the inference of gene regulatory networks (GRNs) from expression data. Although …

In this study, we infer the breast cancer gene regulatory network from gene expression data.
This network is obtained from the application of the BC3Net inference algorithm to a large …

Regulatory interactions mediated by transcription factors (TFs) make up complex networks
that control cellular behavior. Fully understanding these gene regulatory networks (GRNs) …

Cancer is a complex disease that has proven to be difficult to understand on the single-gene
level. For this reason a functional elucidation needs to take interactions among genes on a …

Background Urothelial pathogenesis is a complex process driven by an underlying network
of interconnected genes. The identification of novel genomic target regions and gene targets …

Recent technological advances for the acquisition of multi-omics data have allowed an
unprecedented understanding of the complex intricacies of biological systems. In parallel, a …

Background: Cancer is a complex disease with a lucid etiology and in understanding the
causation, we need to appreciate this complexity. Objective: Here we are aiming to gain …

When considering the variation in the genome, transcriptome, proteome and metabolome,
and their interaction with the environment, every individual can be rightfully considered as a …

Gene networks (GNs) use graphs to represent the interaction relationships between genes.
Large-scale GNs are often sparse and contain hub genes that interact with many other …