Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks.

ABSTRACT: The complexity, scale, and uncertainty in regulatory networks (e.g., gene regulatory networks and microbial networks) regularly pose a huge uncertainty in their models. These uncertainties often cannot be entirely reduced using limited and costly data acquired from the normal condition of systems. Meanwhile, regulatory networks often suffer from the non-identifiability issue, which refers to scenarios where the true underlying network model cannot be clearly distinguished from other possible models. Perturbation or excitation is a well-known process in systems biology for acquiring targeted data to reveal the complex underlying mechanisms of regulatory networks and overcome the non-identifiability issue. We consider a general class of Boolean network models for capturing the activation and

SUBMITTER: Alali M 

PROVIDER: S-EPMC11474162 | biostudies-literature | 2024 Sep-Oct

REPOSITORIES: biostudies-literature