A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …

Molecular interaction networks lay the foundation for studying how biological functions are
controlled by the complex interplay of genes and proteins. Investigating perturbed processes …

Statistical and machine learning (ML)-based methods have recently advanced in
construction of gene regulatory network (GRNs) based on high-throughput biological …

Background High-throughput transcriptomic datasets are often examined to discover new
actors and regulators of a biological response. To this end, graphical interfaces have been …

Highlights•GRNs represent causal relationships among regulators and their downstream
genes.•Biological hypothesis drives data collection and the subsequent inferred …

Gene regulatory networks are composed of sub-networks that are often shared across
biological processes, cell-types, and organisms. Leveraging multiple sources of information …

Motivation Reconstructing gene regulatory networks (GRNs) based on gene expression
profiles is still an enormous challenge in systems biology. Random forest-based methods …

The advancement of single-cell sequencing technology in recent years has provided an
opportunity to reconstruct gene regulatory networks (GRNs) with the data from thousands of …

Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to
understanding how genes affect cellular phenotypes. Yet, despite advances in profiling …

Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions
between genes, resulting from the fundamental biological process of transcription and …