Causal Discovery for Gene Regulatory Network Prediction

01/03/2023
by   Jacob Rast, et al.
0

Biological systems and processes are networks of complex nonlinear regulatory interactions between nucleic acids, proteins, and metabolites. A natural way in which to represent these interaction networks is through the use of a graph. In this formulation, each node represents a nucleic acid, protein, or metabolite and edges represent intermolecular interactions (inhibition, regulation, promotion, coexpression, etc.). In this work, a novel algorithm for the discovery of latent graph structures given experimental data is presented.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/03/2018

Gene regulatory networks: a primer in biological processes and statistical modelling

Modelling gene regulatory networks not only requires a thorough understa...
research
10/12/2020

PhD dissertation to infer multiple networks from microbial data

The interactions among the constituent members of a microbial community ...
research
02/04/2014

Discovering Latent Network Structure in Point Process Data

Networks play a central role in modern data analysis, enabling us to rea...
research
08/20/2019

Flud: a hybrid crowd-algorithm approach for visualizing biological networks

Modern experiments in many disciplines generate large quantities of netw...
research
02/26/2020

Cell cycle and protein complex dynamics in discovering signaling pathways

Signaling pathways are responsible for the regulation of cell processes,...
research
04/06/2023

Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery

Causal discovery of genome-scale networks is important for identifying p...
research
02/02/2012

Global modeling of transcriptional responses in interaction networks

Motivation: Cell-biological processes are regulated through a complex ne...

Please sign up or login with your details

Forgot password? Click here to reset