Generif Quality Assurance as Summary Revision

Lu Z, Cohen KB, Hunter L

Center for Computational Pharmacology, University of Colorado Health Sciences Center, Aurora, CO, 80045, USA
E-mail: fZhiyong.Lu, Kevin.Cohen, Larry.Hunterg @ uchsc.edu


Pac Symp Biocomput. 2007;:269-280.


Abstract

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.


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