A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstractsWestergaard D, Stærfeldt HH, Tønsberg C, Jensen LJ, Brunak S (2018) A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts. PLOS Computational Biology 14(2): e1005962. https://doi.org/10.1371/journal.pcbi.1005962
Showcases the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets.