Reliable machine learning models in genomic medicine using conformal prediction

Christina Papangelou ¹ Konstantinos Kyriakidis ^2* Pantelis Natsiavas ³ Ioanna Chouvarda ¹ Andigoni Malousi ^1,4*

• ¹ School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
• ² Genomics Institute, Baskin School of Engineering, University of California, Santa Cruz, Santa Cruz, California, United States
• ³ Institute of Applied Biosciences (INAB), Thessaloniki, Greece
• ⁴ Center for Interdisciplinary Research and Innovation, Thessaloniki, Greece

Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have lifethreatening impact, raising reasonable skepticism about whether these applications have practical benefit in clinical settings. Conformal prediction offers a versatile framework for addressing these concerns by quantifying the uncertainty of predictive models. In this perspective review, we investigate potential applications of conformalized models in genomic medicine and discuss the challenges towards bridging genomic medicine applications with clinical practice. We also demonstrate the impact of a binary transductive model and a regression-based inductive model in predicting drug response as well as the performance of a multi-class inductive predictor in addressing distribution shifts in molecular subtyping. The main conclusion is that as machine learning and genomic medicine are increasingly infiltrating healthcare services, conformal prediction has the potential to overcome the safety limitations of current methods and could be effectively integrated into uncertainty-informed applications within clinical environments.