Preliminary evaluation of the CellFinder literature curation pipeline for gene expression in kidney cells and anatomical parts by Mariana Neves, Alexander Damaschun, Nancy Mah, Fritz Lekschas, Stefanie Seltmann, Harald Stachelscheid, Jean-Fred Fontaine, Andreas Kurtz, and Ulf Leser. (Database (2013) 2013 : bat020 doi: 10.1093/database/bat020)
Biomedical literature curation is the process of automatically and/or manually deriving knowledge from scientific publications and recording it into specialized databases for structured delivery to users. It is a slow, error-prone, complex, costly and, yet, highly important task. Previous experiences have proven that text mining can assist in its many phases, especially, in triage of relevant documents and extraction of named entities and biological events. Here, we present the curation pipeline of the CellFinder database, a repository of cell research, which includes data derived from literature curation and microarrays to identify cell types, cell lines, organs and so forth, and especially patterns in gene expression. The curation pipeline is based on freely available tools in all text mining steps, as well as the manual validation of extracted data. Preliminary results are presented for a data set of 2376 full texts from which >4500 gene expression events in cell or anatomical part have been extracted. Validation of half of this data resulted in a precision of ∼50% of the extracted data, which indicates that we are on the right track with our pipeline for the proposed task. However, evaluation of the methods shows that there is still room for improvement in the named-entity recognition and that a larger and more robust corpus is needed to achieve a better performance for event extraction.
Database URL: http://www.cellfinder.org/.
Another extremely useful data curation project.
Do you get the impression that curation projects will continue to be outrun by data production?
And that will be the case, even with machine assistance?
Is there an alternative to falling further and further behind?
Such as abandoning some content (CNN?) to simply forever go uncurated? Or the same to be true for government documents/reports?
I am sure we all have different suggestions for what data to dump alongside the road to make room for the “important” stuff.
Suggestions on solutions other than simply dumping data?