Mining experimental evidence of molecular function claims from the literature.
BACKGROUND
The rate at which gene-related findings appear in the scientific literature makes it difficult if not impossible for biomedical scientists to keep fully informed and up to date. The importance of these findings argues for the development of automated methods that can find, extract and summarize this information. This article reports on methods for determining the molecular function claims that are being made in a scientific article, specifically those that are backed by experimental evidence.


RESULTS
The most significant result is that for molecular function claims based on direct assays, our methods achieved recall of 70.7% and precision of 65.7%. Furthermore, our methods correctly identified in the text 44.6% of the specific molecular function claims backed up by direct assays, but with a precision of only 0.92%, a disappointing outcome that led to an examination of the different kinds of errors. These results were based on an analysis of 1823 articles from the literature of Saccharomyces cerevisiae (budding yeast).


BACKGROUND
The annotation files for S.cerevisiae are available from ftp://genome-ftp.stanford.edu/pub/yeast/data_download/literature_curation/gene_association.sgd.gz. The draft protocol vocabulary is available by request from the first author.
Crangle
Colleen E
CE
Converspeech LLC, 60 Kirby Place, Palo Alto, CA 94301, USA. crangle@converspeech.com
Cherry
J Michael
JM
Hong
Eurie L
EL
Zbyslaw
Alex
A
D001185:Artificial Intelligence; D019317:Evidence-Based Medicine / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010506:Periodicals as Topic; D015203:Reproducibility of Results; D029701:Saccharomyces cerevisiae Proteins / Q000145:classification* / Q000378:metabolism*; D012680:Sensitivity and Specificity
D029701:Saccharomyces cerevisiae Proteins