Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46777, first published .
The Alzheimer’s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research

The Alzheimer’s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research

The Alzheimer’s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research

Journals

  1. Sobral M, Weber J, Bizolo G, Santiago H, Valadão F, Gonçalves J, Matos A, Aragão L, Bolwerk M, Sousa I, Barancelli R, Freitas L, Pereira C, Araújo E, Silvano F. ABORDAGENS TERAPÊUTICAS EMERGENTES PARA O TRATAMENTO DA DOENÇA DE ALZHEIMER. Revista Contemporânea 2024;4(5):e4296 View
  2. Matsumoto N, Moran J, Choi H, Hernandez M, Venkatesan M, Wang P, Moore J, Kendziorski C. KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models. Bioinformatics 2024;40(6) View
  3. Shao S, Henrique Ribeiro P, Ramirez C, Moore J. A review of feature selection strategies utilizing graph data structures and Knowledge Graphs. Briefings in Bioinformatics 2024;25(6) View
  4. Theodoropoulos C, Catalin Coman A, Henderson J, Moens M. Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer’s Disease. IEEE Access 2024;12:180652 View
  5. Rasheed N, Hussain H, Rehman Z, Sabir A, Ashraf W, Ahmad T, Alqahtani F, Imran I. Co-administration of coenzyme Q10 and curcumin mitigates cognitive deficits and exerts neuroprotective effects in aluminum chloride-induced Alzheimer's disease in aged mice. Experimental Gerontology 2025;199:112659 View
  6. Matsumoto N, Choi H, Moran J, Hernandez M, Venkatesan M, Li X, Chang J, Wang P, Moore J, Robinson P. ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning. Bioinformatics 2025;41(2) View
  7. Obafemi-Ajayi T, Jennings S, Zhang Y, Liu K, Peckham J, Moore J. AI as an accelerator for defining new problems that transcends boundaries. BioData Mining 2025;18(1) View
  8. Gutiérrez Cruz A, de Anda-Jáuregui G, Hernández-Lemus E. Gene Co-Expression Analysis Reveals Functional Differences Between Early- and Late-Onset Alzheimer’s Disease. Current Issues in Molecular Biology 2025;47(3):200 View
  9. Zagare A, Balaur I, Rougny A, Saraiva C, Gobin M, Monzel A, Ghosh S, Satagopam V, Schwamborn J. Deciphering shared molecular dysregulation across Parkinson’s disease variants using a multi-modal network-based data integration and analysis. npj Parkinson's Disease 2025;11(1) View
  10. Li X, Chang J, Venkatesan M, Wang Z, Moore J. Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling. BioData Mining 2025;18(1) View
  11. Hazelett D, Wren J. Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms. Bioinformatics 2025;41(4) View
  12. Yang Y, Yu K, Gao S, Yu S, Xiong D, Qin C, Chen H, Tang J, Tang N, Zhu H. Alzheimer's disease knowledge graph enhances knowledge discovery and disease prediction. Computers in Biology and Medicine 2025;192:110285 View
  13. Cong C, Milne-Ives M, Ananthakrishnan A, Maetzler W, Meinert E. From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease. Journal of Parkinson’s Disease 2025 View
  14. Raza M, Hassan S, Jamil S, Hyder N, Batool K, Walji S, Abbas M. Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions. Frontiers in Neuroinformatics 2025;19 View
  15. Dobreva J, Simjanoska Misheva M, Mishev K, Trajanov D, Mishkovski I. A Unified Framework for Alzheimer’s Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation. Brain Sciences 2025;15(5):523 View