Predicting Drug-Drug Interactions Using Knowledge Graphs

• URL: http://arxiv.org/abs/2308.04172v2
• Date: Fri, 11 Aug 2023 07:54:24 GMT
• Title: Predicting Drug-Drug Interactions Using Knowledge Graphs
• Authors: Lizzy Farrugia, Lilian M. Azzopardi, Jeremy Debattista and Charlie Abela
• Abstract summary: We propose the medicX end-to-end framework that integrates several drug features from public drug repositories into a Knowledge Graph. Ultimately, we use a Machine Learning (ML) algorithm that predicts unknown Drug-Drug Interactions (DDIs) We also develop a graph auto-encoder model that uses a Graph Neural Network (GNN), which achieved an F1-score of 91.94%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the last decades, people have been consuming and combining more drugs than before, increasing the number of Drug-Drug Interactions (DDIs). To predict unknown DDIs, recently, studies started incorporating Knowledge Graphs (KGs) since they are able to capture the relationships among entities providing better drug representations than using a single drug property. In this paper, we propose the medicX end-to-end framework that integrates several drug features from public drug repositories into a KG and embeds the nodes in the graph using various translation, factorisation and Neural Network (NN) based KG Embedding (KGE) methods. Ultimately, we use a Machine Learning (ML) algorithm that predicts unknown DDIs. Among the different translation and factorisation-based KGE models, we found that the best performing combination was the ComplEx embedding method with a Long Short-Term Memory (LSTM) network, which obtained an F1-score of 95.19% on a dataset based on the DDIs found in DrugBank version 5.1.8. This score is 5.61% better than the state-of-the-art model DeepDDI. Additionally, we also developed a graph auto-encoder model that uses a Graph Neural Network (GNN), which achieved an F1-score of 91.94%. Consequently, GNNs have demonstrated a stronger ability to mine the underlying semantics of the KG than the ComplEx model, and thus using higher dimension embeddings within the GNN can lead to state-of-the-art performance.

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