GAPscreener: A Tool for Screening Genetic Association Literature
Author Information
Author(s): Yu Wei, Clyne Melinda, Dolan Siobhan M, Yesupriya Ajay, Wulf Anja, Liu Tiebin, Khoury Muin J, Gwinn Marta
Primary Institution: National Office of Public Health Genomics, Centers for Disease Control and Prevention
Hypothesis
Can an SVM-based tool improve the efficiency and accuracy of screening genetic association studies in PubMed?
Conclusion
GAPscreener is an effective tool that significantly reduces the manual review burden while improving the accuracy of identifying genetic association literature.
Supporting Evidence
- The SVM tool achieved 97.5% recall and 98.3% specificity in performance testing.
- GAPscreener reduced the number of abstracts requiring individual review by about 90%.
- The tool identified 47 articles missed by traditional screening methods.
Takeaway
GAPscreener is a computer program that helps scientists find important studies about genes and diseases much faster and more accurately than doing it by hand.
Methodology
The study used a support vector machine (SVM) to classify PubMed abstracts, comparing its performance against traditional manual screening methods.
Limitations
The tool may miss some articles and the processing speed could be improved with fewer keywords.
Statistical Information
P-Value
<0.0001
Confidence Interval
(0.958–0.975)
Statistical Significance
p<0.0001
Digital Object Identifier (DOI)
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