Structure-Aware Contrastive Learning for Biomedical Embeddings: Bridging the Gap Between HPO and Clinical Literature
TL;DR
A new embedding adaptation procedure is defined whose fine-tuning approach is guided by a novel "Disease-Overlap" similarity measure, which prioritizes clinical co-occurrence of phenotypes over taxonomic distance, and optimizes the embedding space using AnglE Loss to mitigate gradient saturation.
Abstract
Large Language Models (LLMs) are extensively used at biomedical text processing but often fail to capture the complex, functional relationships encoded in expert knowledge graphs like the Human Phenotype Ontology (HPO). This "semantic gap'" limits their utility in precision medicine tasks such as rare disease diagnosis, where distinguishing overlapping clinical presentations requires understanding underlying pathophysiological connections rather than just surface-level textual similarity. In this work, we propose a Neuro-Symbolic Alignment Framework that bridges this separation by integrating literature-mined specialized phenotypical descriptions with the ontological structure used as reference. Specifically, we augment phenotype representations with automatically selected text fragments from massive corpus of descriptions mined from scientific literature (PubMed), overcoming the typical data scarcity of standard ontology definitions. We define a new embedding adaptation procedure whose fine-tuning approach is guided by a novel "Disease-Overlap" similarity measure, which prioritizes clinical co-occurrence of phenotypes over taxonomic distance, and optimizes the embedding space using AnglE Loss to mitigate gradient saturation. Extensive evaluations show that our approach significantly outperforms state-of-the-art baselines, including SapBERT, on both intrinsic semantic correlation and practical downstream tasks, including synthetic patient disease ranking and solving real cases stored in Phenopacket, where our model achieves x4 top-1 accuracy than the previous best model.