DCI: learning causal differences between gene regulatory networks.

ABSTRACT:

Summary

Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two

SUBMITTER: Belyaeva A 

PROVIDER: S-EPMC9991896 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature