Distributed Bayesian networks reconstruction on the whole genome scale.

ABSTRACT:

Background

Bayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. They have been applied in various biological contexts, including gene regulatory networks and protein-protein interactions inference. Generally, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in cases when the acyclicity of the graph can be externally ensured, it is possible to find the optimal network in polynomial time. While our previously developed tool BNFinder implements polynomial time algorithm, reconstructing networks with the large amount of experimental data still leads to computations on single CPU growing exceedingly.<

SUBMITTER: Frolova A 

PROVIDER: S-EPMC6197044 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature