What does this research mean for the field?

PanKgraph provides a unified, machine-readable knowledge graph that integrates diverse, multi-modal Type 1 Diabetes data to enable cross-scale reasoning and hypothesis generation. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to create a unified platform that integrates diverse data related to Type 1 Diabetes for better research accessibility.

June 7, 2026

2395-P: PanKgraph: An Integrated Knowledge Graph for Type 1 Diabetes Research

Key Points

The aim is to create a unified platform that integrates diverse data related to Type 1 Diabetes for better research accessibility.
Developed PanKgraph as a comprehensive knowledge graph integrating multi-modal data and publications.
Provided a web-based interface for users to interact with T1D knowledge visually and textually.
Implemented an API for large language models to analyze experimental data alongside literature.
PanKgraph allows exploration of T1D knowledge through interactive graphs and visual representations.
Supports flexible integration of various data types, facilitating cross-disciplinary research.
Aims to accelerate insights into T1D mechanisms and potential therapies by lowering data integration barriers.

Abstract

Introduction and Objective: Type 1 Diabetes (T1D) is a lifelong autoimmune disease with major impacts on health, quality of life, and healthcare systems worldwide. Despite decades of research, progress is often slowed by fragmented knowledge spread across isolated databases, publications, and experimental platforms. These resources are rich but heterogeneous, making it difficult to connect genetic, molecular, cellular, clinical, and imaging evidence into a coherent view. Methods: Here we present PanKgraph, a comprehensive knowledge graph for Type 1 Diabetes that integrates diverse data types into a unified, machine-readable framework. Unlike traditional databases, which store data in fixed schemas and silos, a knowledge graph explicitly represents entities and their relationships, enabling flexible integration of multi-modal experimental data, biomedical literature, and curated domain knowledge. This structure supports cross-scale reasoning, hypothesis generation, and discovery of non-obvious connections across biological systems. Results: To serve the broader research community, we provide an open, web-based, question-driven interface that allows users to explore T1D knowledge through interactive graphs, concise textual summaries, and visual representations linked to underlying experimental evidence. We further expose an application programming interface (API) designed for large language models, enabling joint analysis of scientific images, publications, and raw experimental data. Conclusion: By lowering barriers to data integration and interpretation, PanKgraph aims to shift T1D research beyond single-axis thinking, foster collaboration across disciplines, and accelerate innovative, systems-level insights into disease mechanisms and therapies. Disclosure Y. Wang: None. R. Mao: None. F. Feng: None. H.T. Vu: None. Y. Huang: None. Z. Han: None. A.K. Huber: None. J. Cartailler: None. S. Chen: Stock/Shareholder; Current; iOrganBio Inc. Stock/Shareholder; Ended; Oncobeat. M. Brissova: None. S. Parker: Research Support; Current; Pfizer Inc. Consultant; Ended; Novo Nordisk. J. Liu: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5U24DK138515-02)

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