ArticleScientific reports2024
Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
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- Construction and Applications of Knowledge Graphs in Ophthalmology.Ophthalmic research · 2026Review
- AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.Artificial intelligence in medicine · 2025Article
- Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models.Journal of medical Internet research · 2025Article
- Driving research on successful aging and neuroprotection in Latin America: Insights from the inaugural symposium on brain resilience and healthy longevity.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- BiomedRAG: A retrieval augmented large language model for biomedicine.Journal of biomedical informatics · 2025Article
- Benchmarking of Large Language Models for the Dental Admission Test.Health data science · 2025Article
- KnowNet: Guided Health Information Seeking from LLMs via Knowledge Graph IntegrationIEEE transactions on visualization and computer graphics · 2025Article
- FuseLinker: Leveraging LLM's pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs.Journal of biomedical informatics · 2024Article
- Exploring and Comparing Existing Natural Product Databases Towards Whole Person Health Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.Research square · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.medRxiv : the preprint server for health sciences · 2023Article
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Authors and funding
13 authors at 8 institutions in 3 countries.
Funding
Abstract
Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.