10.17632/H83W68YR7G.1
Pijnenburg, Thom
Thom
Pijnenburg
Data for: Discovering Gene-Disease Associations with Biomedical Word Embeddings
Mendeley
2020
Dataset
Drug Discovery
Bioinformatics
FOS: Computer and information sciences
FOS: Computer and information sciences
Natural Language Processing
Machine Learning
Word Embedding
Mitra, Payal
Payal
Mitra
Sazonau, Viachaslau
Viachaslau
Sazonau
2020-12-09
10.17632/h83w68yr7g
License Agreement for granting right to use the publication assets
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.