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Artificial Intelligence Strategies for Alzheimer's Disease Research

PI
Shen, Li
DAR ID
10851
Initial Approval Date
May 30, 2023
Current DAR Status
Active
PI Institution
University of Pennsylvania
PI Institution Country
United States
PI Institution Type
Non-Profit

Requested Datasets (4)

NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00130Large scale CSF proteogenomic atlasNG00131Large scale genetics study on CSF and brain metabolite levelsNG00177ADGC Multi-Ancestry GWAS Meta-Analysis – Rajabli et al., 2025

Research Use Statement
The goal of this project is to develop artificial intelligence (AI) approaches for extracting unforeseen patterns from clinical, genetic, genomic, and imaging data that could lead to ideas for new drug development or drug repurposing. Our proposed AI methods and software will be open-source, user-friendly, and freely available for all to use. Specifically, we will analyze ADSP data sets using three novel informatics methods to tailor our automated machine learning (AutoML) tool to the analysis of Alzheimer’s disease (AD) data. First, we will develop a Multi-Modal Interaction (M2I) feature selection algorithm for identifying genetic interactions that are predictive of AD (AIM 1). Second, we will develop a Knowledge-driven Multi-omics Integration (KMI) algorithm for combining omics features for AI analysis of AD (AIM 2). Third, we will develop a Multidimensional Brain Imaging Omics (MBIO) integration framework for the joint analysis of multisource large-scale data for predicting AD. Finally, we will integrate all three biomedical informatics methods into our open-source AutoML software package and apply it to the ADSP data sets. We expect our methods will reveal new biomarkers for AD that will open the door for better treatments and clinical decision support.

Non-Technical Summary
The goal of this project is to develop artificial intelligence (AI) approaches for extracting unforeseen patterns from clinical, genetic, genomic, and imaging data that could lead to ideas for new drug development or drug repurposing. We will develop three biomedical informatics methods with focuses on genetics, genomics and imaging respectively. We will integrate these methods into our open-source AutoML software package, and apply it to the ADSP data sets. We expect our methods will reveal new biomarkers for AD that will open the door for better treatments and clinical decision support.

Reported Publications (30)

Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.

  1. A genetically informed brain atlas for enhancing brain imaging genomics. Bao J, et al. Nature communications. 2025. PMID: 40229250
  2. Biologically Enhanced Machine Learning Model to uncover Novel Gene-Drug Targets for Alzheimer's Disease. Orlenko A, et al. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2025. PMID: 39670388
  3. CASTER-DTA: equivariant graph neural networks for predicting drug-target affinity. Kumar R, et al. Briefings in bioinformatics. 2025. PMID: 41139314
  4. Connecting intermediate phenotypes to disease using multi-omics in heart failure. Moore A, et al. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2025. PMID: 39670392
  5. ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning. Matsumoto N, et al. Bioinformatics (Oxford, England). 2025. PMID: 39842860
  6. Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare. Shang T, et al. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2025. PMID: 40502235
  7. Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction. Venkatesh R, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2025. PMID: 41231230
  8. Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs. Shang T, et al. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2025. PMID: 40502260
  9. MG-TCCA: Tensor Canonical Correlation Analysis Across Multiple Groups. Zhou Z, et al. IEEE transactions on computational biology and bioinformatics. 2025. PMID: 39348263
  10. Network-based analyses of multiomics data in biomedicine. Kumar R, et al. BioData mining. 2025. PMID: 40426270
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  1. The Impact of Ancestry on Genome-Wide Association Studies. Jones SC, et al. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2025. PMID: 39670375
  2. A review of feature selection strategies utilizing graph data structures and Knowledge Graphs. Shao S, et al. Briefings in bioinformatics. 2024. PMID: 39526853
  3. Assessing the limitations of relief-based algorithms in detecting higher-order interactions. Freda PJ, et al. BioData mining. 2024. PMID: 39354639
  4. Centralized and Federated Models for the Analysis of Clinical Data. Li R, et al. Annual review of biomedical data science. 2024. PMID: 38723657
  5. Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection. Tong B, et al. Machine learning in medical imaging. MLMI (Workshop). 2024. PMID: 38463442
  6. Cross-phenotype associations between Alzheimer's Disease and its comorbidities may provide clues to progression. Moore A, et al. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2024. PMID: 38827078
  7. Deep learning-based approaches for multi-omics data integration and analysis. Ballard JL, et al. BioData mining. 2024. PMID: 39358793
  8. Distance-weighted Sinkhorn loss for Alzheimer's disease classification. Wang Z, et al. iScience. 2024. PMID: 38433927
  9. Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes. Bao J, et al. Annual review of biomedical data science. 2024. PMID: 38848574
  10. Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI. Tong B, et al. AMIA ... Annual Symposium proceedings. AMIA Symposium. 2024. PMID: 40417489
  11. Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery. Hou B, et al. Medical image analysis. 2024. PMID: 38941858
  12. Is the Relationship Between Cardiovascular Disease and Alzheimer's Disease Genetic? A Scoping Review. Moore A, et al. Genes. 2024. PMID: 39766777
  13. KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models. Matsumoto N, et al. Bioinformatics (Oxford, England). 2024. PMID: 38830083
  14. PAGER: A novel genotype encoding strategy for modeling deviations from additivity in complex trait association studies. Freda PJ, et al. BioData mining. 2024. PMID: 39394173
  15. PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge. Hou B, et al. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2024. PMID: 38827072
  16. The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research. Romano JD, et al. Journal of medical Internet research. 2024. PMID: 38635981
  17. Aliro: an automated machine learning tool leveraging large language models. Choi H, et al. Bioinformatics (Oxford, England). 2023. PMID: 37796839
  18. Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline. Tong B, et al. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2023. PMID: 37350880
  19. Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE. Wang X, et al. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2023. PMID: 37350896
  20. Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease. Sha J, et al. Methods (San Diego, Calif.). 2023. PMID: 37507059