Filtering False Positive MicroRNA Candidates Using Clustering
Author Information
Author(s): Leung Wing-Sze, Lin Marie CM, Cheung David W, Yiu SM
Primary Institution: The University of Hong Kong
Hypothesis
Can a clustering-based approach improve the prediction of microRNAs by filtering out false positives?
Conclusion
The clustering-based approach effectively increases the positive predictive value of microRNA prediction programs while maintaining high sensitivity.
Supporting Evidence
- 45.45% of human miRNAs are clustered.
- 51.86% of mouse miRNAs are clustered.
- 48.67% of rat miRNAs are clustered.
- The clustering-based approach raised positive predictive value by 15.23% to 23.19%.
Takeaway
The study shows that grouping similar microRNAs together helps to find the real ones better and avoid mistakes in predictions.
Methodology
The study validated microRNA clustering in genomes and developed a clustering-based approach to filter false positives from a prediction software.
Limitations
The approach may not work well if there are no clustered miRNAs in the sequence.
Statistical Information
P-Value
p<0.0001
Statistical Significance
p<0.0001
Digital Object Identifier (DOI)
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