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Machine Learning upon RDF Knowledge Graphs for Drug Safety: A Case Study on Reactome Data
Kalliopi Kastampolidou1, George I Gavriilidis1, Pantelis Natsiavas1
1Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece.
Abstract:
Artificial Intelligence (AI), particularly Machine Learning (ML), has gained attention for its potential in various domains. However, approaches integrating symbolic AI with ML on Knowledge Graphs have not gained significant focus yet. We argue that exploiting RDF/OWL semantics while conducting ML could provide useful insights. We present a use case using signaling pathways from the Reactome database to explore drug safety. Promising outcomes suggest the need for further investigation and collaboration with domain experts.
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