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Learning to take it personally: Precision drug repurposing through patient-specific loss on knowledge graphs using Biobank data
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DOI：10.1016/j.jbi.2026.105039.png)
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Abstract
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• Algorithm-level personalization. We embed patient context directly into the learning objective via a patient-specific loss that blends standard link prediction with terms guided by polygenic risk scores (PRS) and protein-biomarker deviations, optimizing for an individual rather than the population. • Clinically grounded signals. We integrate UK Biobank–derived PRS, protein biomarker levels, and diagnosis history to tailor drug–disease scores to each patient’s biology and clinical context, anchoring personalization in routinely collectable, real-world data. • Preserved generalization with better rankings. We maintain foundation-model link-prediction quality while substantially improving patient-specific drug repurposing performance (e.g., AUPRC improvements ranging from 1.3 × to 5.4 × across patients), demonstrating effectiveness without sacrificing global metrics. • Interpretability at the patient level. We learn sparse, patient-level weights over diseases and biomarkers that reveal which comorbidities and dysregulated proteins drive recommendations, supporting transparent, clinician-facing interpretation.
Keywords:
Knowledge graphs
Precision medicine
Precision drug repurposing
Drug repurposing
Graph machine learning
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