Artificial Intelligence and Machine Learning Consortium

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Artificial Intelligence and Machine Learning Consortium

In order to utilize the vast amount of data generated by the ADSP and other NIA funded initiatives, the NIA issued Cognitive Systems Analysis of Alzheimer’s Disease Genetic and Phenotypic Data (PAR-19-269) to apply 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.

Funded AI/ML Projects

Alzheimer’s MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery (U01AG073323)

  • MPIs: Feixiong Cheng, Lynn M. Bekris, and James B. Leverenz

Cognitive Computing of Alzheimer’s Disease Genes and Risk (U01AG068214)

  • PI: Olivier Lichtarge

Assessing Alzheimer Disease Risk and Heterogeneity Using Multimodal Machine Learning Approaches (U01AG068221)

  • MPIs: Honghuang Lin and Anita L. DeStefano

Artificial Intelligence Strategies for Alzheimer’s Disease Research (U01AG066833)

  • MPIs: Jason Moore, Marylyn Ritchie, and Li Shen

Causal and integrative deep learning for Alzheimer’s disease genetics (U01AG073079)

  • PI: Wei Pan

Learning the Regulatory Code of Alzheimer’s Disease Genomes (U01AG068880)

  • MPIs: Towfique Raj and David A. Knowles

Ultrascale Machine Learning to Empower Discovery in Alzheimer’s Disease Biobanks (U01AG068057)

  • MPIs: Paul M. Thompson, Christos Davatzikos, Heng Huang, Andrew J. Saykin, and Li Shen

Genetics of Deep-Learning-Derived Neuroimaging Endophenotypes for Alzheimer’s Disease (U01AG070112)

  • MPIs: Degui Zhi, Myriam Fornage, and Shuiwang Ji