GeneRIF quality assurance as summary revision.
Like the primary scientific literature, GeneRIFs exhibit both growth and obsolescence. NLM's control over the contents of the Entrez Gene database provides a mechanism for dealing with obsolete data: GeneRIFs are removed from the database when they are found to be of low quality. However, the rapid and extensive growth of Entrez Gene makes manual location of low-quality GeneRIFs problematic. This paper presents a system that takes advantage of the summary-like quality of GeneRIFs to detect low-quality GeneRIFs via a summary revision approach, achieving precision of 89% and recall of 77%. Aspects of the system have been adopted by NLM as a quality assurance mechanism.
Lu
Zhiyong
Z
Center for Computational Pharmacology, University of Colorado Health Sciences Center; Aurora, CO 80045, USA. Zhiyong.Lu@uchsc.edu
Cohen
K Bretonnel
KB
Hunter
Lawrence
L
D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic / Q000592:standards*; D015233:Models, Statistical; D009317:National Library of Medicine (U.S.); D039781:PubMed / Q000592:standards*; D011786:Quality Control; D014481:United States