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Updated: Apr 30, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Adjuvant databases and knowledge bases for data-driven research: a computational biology perspective
1Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu, Osaka 566-0002, Japan; Institute of Advanced Medical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, Tokushima 770-8503, Japan; Institute for Protein Research, The University of Osaka, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan; Graduate School of Pharmaceutical Sciences, The University of Osaka, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan.
Abstract:
In adjuvant research, which has long relied on experience and trial and error, advancements in technologies in broad areas, including artificial intelligence (AI)/machine learning, enabling data-driven research, have supported the introduction of more evidence-based, efficient approaches. Data-driven research, including systems biology in vaccinology ('systems vaccinology'), requires not only analytical technologies or techniques but also resources such as databases and knowledge bases. This paper introduces currently available adjuvant-related databases, including datasets utilizable for data-driven research and knowledge bases. By categorizing them into quantitative and qualitative data, several examples of different data-driven approaches, such as systems vaccinology, graph neural network for knowledge graph, AI agent based on a large language model, illustrating how each type of data is utilized, are introduced to show the importance of maintenance and management of the databases and knowledge bases.
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