Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics
Abstract
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to learn generalizable and clinically applicable diagnostics. Here, we argue that a causal perspective improves the identification of these challenges and formalizes their relation to the robustness and generalization of machine learning-based diagnostics. To make for a concrete discussion, we focus on a specific, recently established high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs). Through simulations, we illustrate how major biological and experimental factors of the AIRR domain may influence the learned biomarkers. In conclusion, we argue that causal modeling improves machine learning-based biomarker robustness by identifying stable relations between variables and by guiding the adjustment of the relations and variables that vary between populations.
Keywords
Cite
@article{arxiv.2204.09291,
title = {Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics},
author = {Milena Pavlović and Ghadi S. Al Hajj and Chakravarthi Kanduri and Johan Pensar and Mollie Wood and Ludvig M. Sollid and Victor Greiff and Geir Kjetil Sandve},
journal= {arXiv preprint arXiv:2204.09291},
year = {2023}
}