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Predictive Immune Modeling of Solid Tumors

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Published on: February 25, 2020

Reliable machine learning models in genomic medicine using conformal prediction.

Christina Papangelou¹, Konstantinos Kyriakidis², Pantelis Natsiavas³

¹School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Frontiers in Bioinformatics

|March 11, 2025

Summary

Conformal prediction enhances machine learning in genomic medicine by quantifying uncertainty, addressing safety concerns for personalized healthcare. This approach can improve clinical applications and patient outcomes.

Keywords:

conformal prediction genomic medicine machine learning perspective review reliable predictions uncertainty estimate

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Area of Science:

Genomic Medicine
Machine Learning
Computational Biology

Background:

Personalized healthcare relies on machine learning (ML) and genomic medicine for diagnosis, risk stratification, and treatment.
Clinical adoption of ML in healthcare is hindered by concerns over prediction errors and their life-threatening impact.
Quantifying predictive model uncertainty is crucial for safe and reliable clinical decision-making.

Purpose of the Study:

To explore conformal prediction as a framework for quantifying uncertainty in ML models for genomic medicine.
To review challenges and opportunities for integrating conformalized models into clinical practice.
To demonstrate the utility of conformal prediction in predicting drug response and handling distribution shifts in molecular subtyping.

Main Methods:

Investigated conformalized models within a perspective review framework.
Applied binary transductive and regression-based inductive models for drug response prediction.
Utilized a multi-class inductive predictor to address distribution shifts in molecular subtyping.

Main Results:

Conformal prediction provides a versatile method for quantifying uncertainty in ML models.
Demonstrated the impact of conformalized models in predicting drug response and molecular subtyping.
Highlighted the potential of conformal prediction to mitigate safety limitations of current ML methods.

Conclusions:

Conformal prediction can overcome safety limitations associated with ML in genomic medicine.
This framework offers a pathway for integrating uncertainty-informed ML applications into clinical environments.
Conformal prediction is poised to enhance the reliability and practical benefit of personalized healthcare services.