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Capturing the semantics of related biological concepts, such as genes andmutations, is of significant importance to many research tasks in computationalbiology such as protein-protein interaction detection, gene-drug associationprediction, and biomedical literature-based discovery. Here, we propose toleverage state-of-the-art text mining tools and machine learning models tolearn the semantics via vector representations (aka. embeddings) of over400,000 biological concepts mentioned in the entire PubMed abstracts. Ourlearned embeddings, namely BioConceptVec, can capture related concepts based ontheir surrounding contextual information in the literature, which is beyondexact term match or co-occurrence-based methods. BioConceptVec has beenthoroughly evaluated in multiple bioinformatics tasks consisting of over 25million instances from nine different biological datasets. The evaluationresults demonstrate that BioConceptVec has better performance than existingmethods in all tasks. Finally, BioConceptVec is made freely available to theresearch community and general public viahttps://github.com/ncbi-nlp/BioConceptVec.

BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale