Interpretable AI for inference of causal molecular relationships from omics data.

ABSTRACT: The discovery of molecular relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model, under certain assumptions, estimates an average of a causal quantity reflecting the direct influence of one variable on another. We leverage this insight to propose a precise definition of a gene regulatory relationship and implement a new tool, CIMLA (Counterfactual Inference by Machine Learning and Attribution Models), to identify differences in gene regulatory networks between biological conditions, a problem that has received great attention in recent years. Using extensive benchmarking on simulated dat

SUBMITTER: Dibaeinia P 

PROVIDER: S-EPMC11827637 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature