One major challenge in Type 2 Diabetes (T2D) is the enormous heterogeneity associated with the disease. This pilot funding program, through the NIDDK Information Network (dkNET, https://dknet.org), is designed to capitalize on the rapid advances in AI and other data science areas in addressing major gaps in the study of Type 2 Diabetes (T2D) heterogeneity, including opportunities in extracting knowledge from the enormous amount of data and literature, and in recruiting AI experts to this area of domain science. It calls for multi-disciplinary teams to: 1) develop AI foundation models for T2D; (2) validate the models with top research questions in T2D heterogeneity; (3) disseminate the models and engage the community for further development, validation and application; and (4) develop use cases that demonstrate the models’ potential in accelerating research in diabetes heterogeneity study. The teams are expected to work with dkNET to coordinate their projects, so that awardees can integrate and share products through the dkNET platform.

The pilot funding will provide opportunities to recruit multidisciplinary teams that include both T2D and AI experts, and to develop foundation models in areas including but not limited to:

Information Extraction

• Data-efficient generative AI models for information extraction from unstructured text, and approaches that seek data efficiency and domain adaptability
• Al models that reframe knowledge extraction as a natural language generation task, allowing domain experts to use natural language descriptions to incorporate specific domain knowledge, such as the meanings of entities and relationships, into the information extraction model
• Approaches to facilitate retrieval-augmented generation (RAG) for biomedical LLMs
• Approaches to use the extracted information to augment the training of the foundation model of multimodal datasets

Representation Learning and Explainability

• Scalable, continually expanding, and factually accurate knowledge graphs comprising concepts from biomedical literature
• Knowledge graph embeddings, multimodal representation learning
• Logic and probabilistic reasoning
• Explainable AI, transparent inference mechanism
• Ethics and bias mitigation

T2D Heterogeneity and Multimodal data

• Approaches tackle systems level multi-organ/multi-scale modeling challenges in mapping the heterogeneities in biological and physiological processes and how they impact T2D etiology, pathology, and prognosis
• Precision disease subtyping with multimodal data and molecular biomarkers
• Models to assess and understand the heterogeneity in intervention effect at individual and in community levels
• Models to support study of T2D associated health disparities and the impact on disease heterogeneity

Clinical Decision Support

• Decision support system for clinicians and patients based on the subtypes of T2D
• Models to predict and guide intervention and response such as physical activity and dietary prescriptions
• Models to improve the diagnostic, prognostic, and therapeutic value of Continuous Glucose Monitoring (CGM) profiles in all persons with dysglycemia, and to define digital markers of heterogeneity from CGM profiles together patient-specific factors (e.g., SDOH, age, comorbidities)
• Interpretation and integration of real-time data from CGM and other wearables technology for real-time monitoring of behavioral and physiological parameters to explore diabetes heterogeneity, and to create and assess just-in-time interventions
• Models and digital biomarkers to understand psycho-social-behavioral components of T2D heterogeneity

Given the limited funds available and the pilot nature of the program:

• The application should aim at developing a set of performance metrics for data sets and AI models at the “proof-of-concept” level.
• Applications proposing the purchase of GPUs, or allocation of a significant portion of the budget for cloud resources will not be considered responsive.
• Collecting primary data will not be considered responsive.

Applications should also demonstrate willingness and plans to work with dkNET to coordinate model development, model sharing, AI ethics, co-design with stakeholders, community engagement, and use case development. dkNET, the NIDDK Information Network (https://dknet.org), is a program that supports the NIDDK community’s needs by providing an information portal that connects users to data, analytical tools, and other biomedical research resources. Additionally, dkNET supports researchers by providing a hub for data-driven hypothesis generation; a suite of tools that assist users in FAIR (Findable, Accessible, Interoperable, Reusable) practice, and in improving rigor and reproducibility in research; and a variety of programs to enhance community engagement and workforce development. dkNET previously successfully ran and managed the dkNET New Investigator Pilot Program in Bioinformatics. dkNET program is currently developing an open data science platform to enable the community to participate in AI model development, to share their models, and to develop use cases.

Letter of Intent Due Date: October 11, 2024 [LOI should be submitted before 8:59 pm PDT (11:59 pm EDT)]

Application Due Date: November 12, 2024 [application should be submitted before 8:59 pm PST (11:59 pm EST)]

Source and more information: https://dknet.org/about/ai-pilot