The AI/ML Consortium was established in 2021 to support the application of cognitive systems to the vast genetic, imaging, biomarker, and clinical data generated by ADSP and other NIA-funded initiatives. The collection of projects funded through the Consortium use artificial intelligence and machine learning approaches to achieve a number of goals, including, but not limited to, advancing gene discovery, modeling variant/gene action, building prognostic models based on biomarkers, and identifying opportunities for drug repurposing.
Associated Groups
The ADSP AI/ML Consortium is applying cognitive systems approaches to the analysis of AD genetic and related data. Analysis of the data generated and harmonized by the ADSP will help to identify new genes and genetic pathways that will reveal risk and protective factors for AD and guide the field toward novel therapeutic approaches to the disease. The AI/ML Working Group meets to share research updates on AI/ML Consortium projects and discuss collaborations within the AI/ML Consortium and between AI/ML investigators and other ADSP components.
The AI/ML Executive Committee comprises the AI/ML Consortium MPIs and NIA program officers. They meet to discuss administrative topics for the Consortium.
Associated Grants (Current)
| Project # (Links to RePORTER) | Grant Name | Investigators | FOA(s) | Project Start Date | Project End Date |
|---|---|---|---|---|---|
| U01AG068214 | Cognitive Computing of Alzheimer’s Disease Genes and Risk | Contact PI:
|
PAR-19-269 | ||
| U01AG070112 | Genetics of Deep-Learning-Derived Neuroimaging Endophenotypes for Alzheimer’s Disease | Contact PI:
|
PAR-19-269 | ||
| U01AG073323 | Alzheimer’s MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery | Contact PI:
|
PAR-19-269 | ||
| U01AG079847 | AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer’s disease via deep learning | Contact PI:
|
PAR-19-269 | ||
| U01AG068057 | Ultrascale Machine Learning to Empower Discovery in Alzheimer’s Disease Biobanks | Contact PI:
|
PAR-19-269, PAR-22-362 |
Associated Grants (Past)
| Project # (Links to RePORTER) | Grant Name | Investigators | FOA(s) | Project Start Date | Project End Date |
|---|---|---|---|---|---|
| U01AG068221 | Assessing Alzheimer Disease Risk and Heterogeneity Using Multimodal Machine Learning Approaches | Contact PI:
|
PAR-19-269 | ||
| U01AG066833 | Artificial Intelligence Strategies for Alzheimers Disease Research | Contact PI:
|
PAR-19-056 | ||
| U01AG073079 | Causal and integrative deep learning for Alzheimers disease genetics | Contact PI:
|
PAR-19-269 | ||
| U01AG068880 | Learning the Regulatory Code of Alzheimer’s Disease Genomes | Contact PI:
|
PAR-19-269 |