ArticlebioRxiv : the preprint server for biology2025
VaxjoGNN: A Graph Neural Network for Ontology-Grounded Vaccine Adjuvant Recommendation.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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Abstract
The selection of an effective adjuvant is a critical bottleneck in vaccine development, particularly for emerging diseases where experimental data is sparse. While computational methods exist for antigen discovery, the task of directly ranking adjuvants for a given disease context remains largely unaddressed. In this work, we frame disease-adjuvant selection as a top-k recommendation task on a heterogeneous knowledge graph grounded in biomedical ontologies. We propose
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.