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Drug Repurposing using Artificial Intelligence and Machine Learning Methods for Alzheimer’s Disease Treatment

Fadaei, Sajedeh | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58762 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Shamloo, Amir; Rohban, Mohammad Hossein
  7. Abstract:
  8. Drug repurposing is a cost-effective and time-saving strategy for discovering new therapeutic uses for approved drugs, reducing development time from 10-15 years to 3-5 years. Knowledge graphs have emerged as powerful tools for representing complex biomedical relationships, integrating molecular interactions, pathway information, and clinical outcomes. Their ability to capture multifaceted drug-disease-target interactions makes them invaluable for drug repurposing, as they can uncover hidden patterns and potential therapeutic applications through network analysis. However, conventional approaches, particularly random walk-based methods, suffer from significant limita- tions: they are inherently random, lack a comprehensive understanding of context, and often fail to fully grasp the rich and complex structure of knowledge graphs, especially in sparse regions of the graph. In this research, we propose a novel framework that leverages graph neural networks for drug repurposing applications. Graph neural networks can effectively learn hierarchical representations by systematically aggregating local and global graph information through multiple message-passing layers, enabling the capture of intricate interaction patterns at various biological levels. To enhance node embeddings, we integrate semantic features extracted from large language models, including BioBERT and GPT, and address a critical gap in traditional approaches by incorporating unstructured textual information from the medical literature
  9. Keywords:
  10. Drug Repurposing ; Alzheimer ; Knowledge Graph-Based Methods ; Graph Neural Network ; Large Language Model

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