ArticleBMC bioinformatics2022
Mining on Alzheimer's diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- AI-derived constrained conditional model for screening marker genes through integrated high-throughput transcriptome big data.BMC medical research methodology · 2026Article
- Bridging the computational-experimental gap: leveraging large language model to prioritize Alzheimer's therapeutics based on comparison of learning models.npj health systems · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- A novel computational analysis integrating social determinants information from EHR and literature with Alzheimer's disease biological knowledge through large language models and knowledge graphs.Innovation in aging · 2025Article
- Bridging the Computational-Experimental Gap: Leveraging Large Language Model to Prioritize Alzheimer's Therapeutics Based on Comparison of Learning Models.Research square · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Alzheimer's disease knowledge graph enhances knowledge discovery and disease prediction.Computers in biology and medicine · 2025Article
- A Unified Framework for Alzheimer's Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation.Brain sciences · 2025Review
- Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics.Frontiers in neurology · 2025Review
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- Knowledge graph and its application in the study of neurological and mental disorders.Frontiers in psychiatry · 2025Review
- DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.JMIR aging · 2024Article
- The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.Journal of medical Internet research · 2024Article
- Knowledge graph embeddings in the biomedical domain: are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks.Bioinformatics advances · 2024Review
- Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions.Yearbook of medical informatics · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundTo date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study the relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.
resultsAmong three knowledge graph completion models, TransE outperformed the other two (MR = 10.53, Hits@1 = 0.28). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge.
conclusionThis paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.
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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.