Construct Semantic Type of 'Gene-mutation-disease' Relation by Computer-aided Curation from Biomedical Literature

摘要

Background: Current semantic type of "gene-mutation-disease" relation lacks fine-grained classification and corresponding relation signal words, which limits its usage in relation extraction from biomedical literature using text mining approach. Methods: We propose a computer-aided curation pipeline in which open relation extraction, signal word clustering, relation type mapping are used to analyze biomedical abstracts for semantic type of "gene-mutation-disease" construction. Coverage metrics are used to evaluate the defined relation type while ClinVar is chosen as a target to test our semantic type's usability and performance on guiding relation extraction from biomedical literature. Results: We have constructed a 5-layer and 16-category semantic type of "gene-mutation-disease" relation with a vocabulary list containing 58 commonly used relation signal words. The vocabulary list has coverage of 95.08% and the semantic type has coverage of 94.12%. From 25 abstracts linked to 30 ClinVar records, 15 relations are correctly mapped and 8 novel relations are discovered additionally. Conclusion: The results show that our semantic type can cover the main relations between "gene", "mutation" and "disease" and can achieve good performance on guiding relation extraction from biomedical text even using relatively out-of-date dictionary-based text mining methods.