Recognition of genetic mutations in text using deep learning

Knowledge about genetic mutations and their impact on the organism is continuously being produced and communicated through scientific publications. This information is then collected by curated databases and integrated in structured knowledge resources, facilitating its discovery and reuse. To aid this work, information extraction methods are increasingly being integrated in the database curation pipelines. This work describes an information extraction method based on deep neural networks for the recognition of mutation mentions in literature abstracts. When applied to the tmVar dataset, the character based model reached an F-measure of 0.874. This result was achieved without use of knowledge resources or any handcrafted features.