Accelerating literature curation with text-mining tools: a case study of using PubTator to curate genes in PubMed abstracts by Chih-Hsuan Wei, Bethany R. Harris, Donghui Li, Tanya Z. Berardini, Eva Huala, Hung-Yu Kao and Zhiyong Lu.
Abstract:
Today’s biomedical research has become heavily dependent on access to the biological knowledge encoded in expert curated biological databases. As the volume of biological literature grows rapidly, it becomes increasingly difficult for biocurators to keep up with the literature because manual curation is an expensive and time-consuming endeavour. Past research has suggested that computer-assisted curation can improve efficiency, but few text-mining systems have been formally evaluated in this regard. Through participation in the interactive text-mining track of the BioCreative 2012 workshop, we developed PubTator, a PubMed-like system that assists with two specific human curation tasks: document triage and bioconcept annotation. On the basis of evaluation results from two external user groups, we find that the accuracy of PubTator-assisted curation is comparable with that of manual curation and that PubTator can significantly increase human curatorial speed. These encouraging findings warrant further investigation with a larger number of publications to be annotated.
Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/
Presentation on PubTator (slides, PDF).
Hmmm, curating abstracts. That sounds like annotating subjects in documents doesn’t it? Or something very close. 😉
If we start off with a set of subjects, that eases topic map authoring because users are assisted by automatic creation of topic map machinery. Creation triggered by identification of subjects and associations.
Users don’t have to start with bare ground to build a topic map.
Clever users build (and sell) forms, frames, components and modules that serve as the scaffolding for other topic maps.