Data for: Discovering Gene-Disease Associations with Biomedical Word Embeddings

Published: 9 December 2020| Version 1 | DOI: 10.17632/h83w68yr7g.1


This is the dataset supporting the publication Discovering Gene-Disease Associations with Biomedical Word Embeddings. Finding the right target for a disease is critical in the drug development process. This paper presents a machine learning approach for predicting gene-disease associations that (i) employs biomedical word embeddings as features for a classifier trained on Open Targets Platform (OTP) data that (ii) generalises beyond a specific disease or gene class. We train, evaluate and compare different word embedding models and classifiers for the task at hand. In addition, we validate the approach by training on a past OTP release and show that it can assist in identifying probable positive associations among current low evidence associations, confirmed by a recent OTP release. Furthermore, we train word embedding models on different time slices of biomedical articles from ScienceDirect and demonstrate that the trained classifier predicts associations that have not explicitly been mentioned in the training corpus, 5 years into the future. Please send a message to Elsevier describing briefly your request on how you would like to use the assets with a short justification. Elsevier will connect directly with you for the elaboration of a personalized license. The contact information can be found in the license information.

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Steps to reproduce

Detailed outline of procedure to reproduce can be found in the README.MD, included in both top level of this repository as well as in the source code package gda-biomed-embeddings-1.0.0.tar.gz


Elsevier BV


Drug Discovery, Bioinformatics, Natural Language Processing, Machine Learning, Word Embedding