%0 Journal Article
%T Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability
%A Alexis Allot
%A Chih-Hsuan Wei
%A Robert Leaman
%A Zhiyong Lu
%J PLOS Biology: A Peer-Reviewed Open-Access Journal
%D 2020
%R 10.1371/journal.pbio.3000716
%X Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide outstanding results. We describe 10 straightforward writing tips¡ªand a web tool, PubReCheck¡ªguiding authors to help address the most common cases that remain difficult for text-mining tools. We anticipate these guides will help authors¡¯ work be found more readily and used more widely, ultimately increasing the impact of their work and the overall benefit to both authors and readers. PubReCheck is available at http://www.ncbi.nlm.nih.gov/research/pubrecheck
%K Text mining
%K Acute myeloid leukemia
%K Species delimitation
%K Chromosome mapping
%K Controlled vocabularies
%K Epstein-Barr virus
%K Mouse models
%K Ocular tumors
%U https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000716