<ul><li>A novel framework has been proposed for integrating multi-modal data in Alzheimer's disease research using large language models and knowledge graphs.</li><li>The approach allows for population-level integration of various data types from independent cohorts without the need for matched patient IDs.</li><li>Statistical analysis identified significant features in each modality, which were connected as nodes in a knowledge graph for further analysis.</li><li>The framework revealed novel relationships and correlations between different data modalities, providing new insights into Alzheimer's disease pathology.</li></ul>

Multi-modal Integration Analysis of Alzheimer's Disease Using Large Language Models and Knowledge Graphs

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