Causal Discovery for Gene Regulatory Network Prediction
This work addresses the challenge of modeling biological regulatory networks for researchers in bioinformatics and systems biology, but appears incremental as it builds on existing graph-based methods without specifying breakthroughs.
The paper tackles the problem of predicting gene regulatory networks by developing a novel algorithm for discovering latent graph structures from experimental data, aiming to represent complex biological interactions as graphs.
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.