A5096912084- Bioinformatics and Genomic Networks
- Advanced Graph Neural Networks
- Computational Drug Discovery Methods
- Biomedical Text Mining and Ontologies
- Data Quality and Management
- Semantic Web and Ontologies
Universidad Politécnica de Madrid
2024-2025
European Telecommunications Standards Institute
2024
<title>Abstract</title> This paper investigates the impact of restructuring knowl- edge graphs (KGs) with well-founded conceptual models to improve ma- chine learning (ML) predictions, particularly in drug repurposing appli- cations. These were developed using OntoUML, which is grounded Unified Foundational Ontology, and constructed following an established workflow for data FAIRification–a process aimed at making more Findable, Accessible, Interoperable, Reusable. We compared performance a...
Artificial Intelligence (AI)-based drug repurposing is an emerging strategy to identify candidates treat rare diseases. However, cutting-edge algorithms based on Deep Learning (DL) typically dont provide a human understandable explanation supporting their predictions. This problem because it hampers the biologists ability decide which predictions are most plausible test in costly lab experiments. In this study, we propose rd-explainer novel AI method for diseases obtains possible together...
<p class="first" dir="auto" id="d3846950e97">The exploration of drug repurposing as an innovative and efficient strategy for discovering new treatments across a spectrum diseases has gained considerable attention in the last few years. One major fields in-silico utilizes Knowledge Graphs (KGs) powerful tools identifying potential candidates. In this work, we make use NeDRex KG [1] to build pipeline/workflow that can be used by researchers generate These candidates are produced training Graph...