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• W2885403013 abstract "Abstract Motivation Drug repurposing is the problem of finding new uses for known drugs, and may either involve finding a new protein target or a new indication for a known mechanism. Several computational methods for drug repurposing exist, and many of these methods rely on combinations of different sources of information, extract hand-crafted features and use a computational model to predict targets or indications for a drug. One of the distinguishing features between different drug repurposing systems is the selection of features. Recently, a set of novel machine learning methods have become available that can efficiently learn features from datasets, and these methods can be applied, among others, to text and structured data in knowledge graphs. Results We developed a novel method that combines information in literature and structured databases, and applies feature learning to generate vector space embeddings. We apply our method to the identification of drug targets and indications for known drugs based on heterogeneous information about drugs, target proteins, and diseases. We demonstrate that our method is able to combine complementary information from both structured databases and from literature, and we show that our method can compete with well-established methods for drug repurposing. Our approach is generic and can be applied to other areas in which multi-modal information is used to build predictive models. Availability https://github.com/bio-ontology-research-group/multi-drug-embedding Contact robert.hoehndorf@kaust.edu.sa" @default.
• W2885403013 created "2018-08-22" @default.
• W2885403013 creator A5043808311 @default.
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• W2885403013 date "2018-08-06" @default.
• W2885403013 modified "2023-09-24" @default.
• W2885403013 title "Drug repurposing through joint learning on knowledge graphs and literature" @default.
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• W2885403013 doi "https://doi.org/10.1101/385617" @default.
• W2885403013 hasPublicationYear "2018" @default.
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