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Drug repurposing methods can identify already approved drugs to treat them efficiently, reducing development costs and time. At the same time, knowledge graph embedding techniques can encode biological information in a single structure that allows users to operate relationships, extract information, learn connections, and make predictions to discover potential new relationships between existing drugs and vector-borne diseases.

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1) Outputs a drug ranking prediction based on a chosen disease and embedding model. 2) Includes the outcomes of predictions on specific diseases generated by trained embedding models using the DRKG dataset. 3) Trains an embedding model on a given dataset. Predictions can be performed on the results of this training. 4) Performs predictions on the already trained embedding models.

See owner's GitHub Repo for more information: https://github.com/dlopezyse/Drug-Repurposing-using-KGE/tree/main

This work is based on the paper: Drug Repurposing Using Knowledge Graph Embeddings with a Focus on Vector-Borne Diseases: A Model Comparison as developed by Diego López Yse and Diego Torres for the Conference on Cloud Computing, Big Data & Emerging Topics 2023.

This application was not uploaded by the author, but through their publicly available Github repository (https://github.com/dlopezyse/Drug-Repurposing-using-KGE/tree/main).

MIT License

Copyright (c) 2023 Diego Lopez Yse

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


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