Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems.

ABSTRACT:

Background

Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformatic methods of causal inference cannot identify the nonlinear relationships and estimate their effect size.

Results

To overcome these limitations, we developed the first computational method that explicitly learns nonlinear causal relations and estimates the effect size using a deep neural network approach coupled with the knockoff framework, named causal directed acyclic graphs using deep learning variable selection (DAG-deepVASE). Using simulation data of diverse scenarios and identifying known and novel causal relations in molec

SUBMITTER: Fan Z 

PROVIDER: S-EPMC10316696 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature