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Human medical data can be challenging to obtain due to data privacy concerns,difficulties conducting certain types of experiments, or prohibitive associatedcosts. In many settings, data from animal models or in-vitro cell lines areavailable to help augment our understanding of human data. However, this datais known for having low etiological validity in comparison to human data. Inthis work, we augment small human medical datasets with in-vitro data andanimal models. We use Invariant Risk Minimisation (IRM) to elucidate invariantfeatures by considering cross-organism data as belonging to differentdata-generating environments. Our models identify genes of relevance to humancancer development. We observe a degree of consistency between varying theamounts of human and mouse data used, however, further work is required toobtain conclusive insights. As a secondary contribution, we enhance existingopen source datasets and provide two uniformly processed, cross-organism,homologue gene-matched datasets to the community.

Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery