The Precision Medicine Track in BioCreative VI aims to bring together the BioNLP community for a novel challenge focused on mining the biomedical literature in search of mutations and protein-protein interactions (PPI). In order to support this track with an effective training dataset with limited curator time, the track organizers carefully reviewed PubMed articles from two different sources: curated public PPI databases, and the results of state-of-the-art public text mining tools. We detail here the data collection, manual review and annotation process and describe this training corpus characteristics. We also describe a corpus performance baseline. This analysis will provide useful information to developers and researchers for comparing and developing innovative text mining approaches for the BioCreative VI challenge and other Precision Medicine related applications.
Precision Medicine Track of BioCreative VI REF introduced a large dataset of protein-protein interactions that are affected by mutations.
BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
BioNLP
Computer Science Engineering