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- W4382786328 abstract "Abstract This paper applies different link prediction methods on a knowledge graph generated from biomedical literature, with the aim to compare their ability to identify unknown drug-gene interactions and explain their predictions. Identifying novel drug–target interactions is a crucial step in drug discovery and repurposing. One approach to this problem is to predict missing links between drug and gene nodes, in a graph that contains relevant biomedical knowledge. Such a knowledge graph can be extracted from biomedical literature, using text mining tools. In this work, we compare state-of-the-art graph embedding approaches and contextual path analysis on the interaction prediction task. The comparison reveals a trade-off between predictive accuracy and explainability of predictions. Focusing on explainability, we train a decision tree on model predictions and show how it can aid the understanding of the prediction process. We further test the methods on a drug repurposing task and validate the predicted interactions against external databases, with very encouraging results." @default.
- W4382786328 created "2023-07-02" @default.
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- W4382786328 date "2023-06-30" @default.
- W4382786328 modified "2023-10-14" @default.
- W4382786328 title "Comparing methods for drug–gene interaction prediction on the biomedical literature knowledge graph: performance versus explainability" @default.
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- W4382786328 doi "https://doi.org/10.1186/s12859-023-05373-2" @default.
- W4382786328 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37391722" @default.
- W4382786328 hasPublicationYear "2023" @default.
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