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

• Main Authors: O'Donoghue, Odhran, Duckworth, Paul, Ughi, Giuseppe, Scheibenreif, Linus, Khezeli, Kia, Hoarfrost, Adrienne, Budd, Samuel, Foley, Patrick, Chia, Nicholas, Kalantari, John, Mackintosh, Graham, Soboczenski, Frank, Sanders, Lauren
• Format: Journal Article
• Language: English
• Published: 14-11-2021
• Subjects: Computer Science - Cryptography and Security; Computer Science - Learning
• DOI: 10.48550/arxiv.2111.07348

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