(2023). A Graph-Based Approach to Integrate Large-Scale Drug and Protein Data for Alzheimer’s Disease Drug Repurposing. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4584–4587). IEEE. https://doi.org/10.1109/bigdata59044.2023.10386766
Περίληψη
Alzheimer’s Disease (AD) remains a formidable challenge in neurodegenerative research, necessitating innovative approaches to uncover novel therapeutic strategies. This study presents a graph-based approach to integrate large-scale drug and protein data, aiming to identify potential drug repurposing candidates for AD. Our methodology constructs a comprehensive graph incorporating protein-protein interactions and drug-protein relations, providing a multifaceted view of the intricate relationships within the biological and pharmacological landscape. By leveraging this graph, we conduct an in-depth analysis to explore various drug repurposing possibilities, focusing on the alignment of AD-related single-cell transcriptomic data. Our approach enables the identification of promising drug candidates by examining the connectivity and interaction patterns within the graph, revealing potential therapeutic targets and drug synergies that may be beneficial for AD treatment. The integration of diverse data types allows for a more holistic understanding of the underlying molecular mechanisms and drug interactions. Through this graph-based analysis, we uncover several promising drug repurposing candidates, providing a foundation for further experimental validation and clinical investigation. This study underscores the potential of leveraging large-scale data and graph-based methodologies in drug repurposing efforts for neurodegenerative diseases, contributing to the advancement of therapeutic research in AD. Our findings illuminate the importance of integrative data analysis in biomedical research, paving the way for the development of more effective and targeted therapeutic interventions for AD.
- DOI
- 10.1109/bigdata59044.2023.10386766
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2023
Σύνδεσμοι
BibTeX
@inproceedings{dimitrakopoulos2023graph,
title = {A Graph-Based Approach to Integrate Large-Scale Drug and Protein Data for Alzheimer’s Disease Drug Repurposing},
author = {Georgios N. Dimitrakopoulos and Konstantinos Lazaros and Marios G. Krokidis and Themis Exarchos and Aristidis G. Vrahatis and Panagiotis Vlamos},
url = {https://doi.org/10.1109/bigdata59044.2023.10386766},
doi = {10.1109/bigdata59044.2023.10386766},
year = {2023},
date = {2023-01-01},
booktitle = {2023 IEEE International Conference on Big Data (BigData)},
pages = {4584–4587},
publisher = {IEEE},
abstract = {Alzheimer’s Disease (AD) remains a formidable challenge in neurodegenerative research, necessitating innovative approaches to uncover novel therapeutic strategies. This study presents a graph-based approach to integrate large-scale drug and protein data, aiming to identify potential drug repurposing candidates for AD. Our methodology constructs a comprehensive graph incorporating protein-protein interactions and drug-protein relations, providing a multifaceted view of the intricate relationships within the biological and pharmacological landscape. By leveraging this graph, we conduct an in-depth analysis to explore various drug repurposing possibilities, focusing on the alignment of AD-related single-cell transcriptomic data. Our approach enables the identification of promising drug candidates by examining the connectivity and interaction patterns within the graph, revealing potential therapeutic targets and drug synergies that may be beneficial for AD treatment. The integration of diverse data types allows for a more holistic understanding of the underlying molecular mechanisms and drug interactions. Through this graph-based analysis, we uncover several promising drug repurposing candidates, providing a foundation for further experimental validation and clinical investigation. This study underscores the potential of leveraging large-scale data and graph-based methodologies in drug repurposing efforts for neurodegenerative diseases, contributing to the advancement of therapeutic research in AD. Our findings illuminate the importance of integrative data analysis in biomedical research, paving the way for the development of more effective and targeted therapeutic interventions for AD.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}