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

ABSTRACT: A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein interaction prediction and literature-based discovery. Concept embeddings-which involve the learning of vector representations of concepts using machine learning models-have been employed to capture the semantics of concepts. To develop concept embeddings, named-entity recognition (NER) tools are first used to identify and normalize concepts from the literature, and then different machine learning models are used to train the embeddings. Despite multiple attempts, existing biomedical concept embeddings generally suffer from suboptimal NER tools, sma

SUBMITTER: Chen Q 

PROVIDER: S-EPMC7237030 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature