Unlocking the Future of Healthcare

Abstract

Personalized medicine leverages patient-specific biological data to tailor prevention, diagnosis, and treatment. Biomarkers are critical for enabling this precision approach. However, biomarker development faces challenges in discovery, validation, and robust modelling, often requiring extensive labelled data. Machine learning (ML) methods like few-shot and zero-shot learning offer potential solutions by enabling model generalization from limited examples. This chapter provides comprehensive exploration of biomarker types and applications and how few-shot and zero-shot techniques could enhance biomarker prediction tasks. Few-shot learning shows promise for biomarker discovery and validation by transferring knowledge from established biomarkers. Zero-shot learning provides opportunities to detect novel biomarker candidates unconstrained by predefined labels. While nascent, few-shot and zero-shot learning present intriguing paradigms for more efficient biomarker modelling, which could accelerate progress towards personalized medicine.