Reconstructing Molecular Networks by Causal Diffusion Do-Calculus Analysis with Deep Learning.

ABSTRACT: Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and biological networks rely on association studies or observational causal-analysis approaches. This study introduces a novel approach that combines intervention operations and diffusion models within a do-calculus framework by deep learning, i.e., Causal Diffusion Do-calculus (CDD) analysis, to infer causal networks between molecules. CDD can extract causal relations from observed data owing to its intervention operations, thereby significantly enhancing the accuracy and generalizability of causal network inference. Computationally, CDD has been

SUBMITTER: Wang J 

PROVIDER: S-EPMC11633463 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature