Correction: genetic algorithm learning as a robust approach to RNA editing site site prediction
2006
Correction on RNA Editing Site Prediction Using Genetic Algorithms
Sample size: 455
publication
Evidence: moderate
Author Information
Author(s): Thompson James, Gopal Shuba
Primary Institution: Rochester Institute of Technology
Conclusion
The correction of a data error did not significantly change the overall conclusions of the original study on RNA editing site prediction.
Supporting Evidence
- The corrected data set used randomly selected non-edited cytosines as true negatives.
- The overall accuracy of the REGAL method is reported to be 86% within the 90% credible intervals.
- Sensitivity and specificity measures were improved with the new fitness function.
Takeaway
The researchers fixed a mistake in their data about RNA editing sites, but their main findings are still the same.
Methodology
The study used genetic algorithms to predict RNA editing sites, employing cross-validation and a new fitness function based on effect size statistics.
Limitations
The study's results are based on corrected data, and the previous miscalculation may have affected earlier conclusions.
Statistical Information
Confidence Interval
90%
Digital Object Identifier (DOI)
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