QiSampler: A Tool for Evaluating Scoring Schemes in High-Throughput Datasets
Author Information
Author(s): Fontaine Jean F, Suter Bernhard, Andrade-Navarro Miguel A
Primary Institution: Max Delbrück Center for Molecular Medicine
Hypothesis
Can QiSampler effectively evaluate scoring schemes for high-throughput biological datasets using a repetitive sampling strategy?
Conclusion
QiSampler is a useful tool for selecting the best scoring schemes for high-throughput datasets.
Supporting Evidence
- QiSampler was applied to a public PPI dataset to select the best scoring scheme.
- QiSampler demonstrated functional enrichment in genes related to oxidoreductase activity in thyroid adenoma samples.
- The tool is implemented as an open-source R script and a web server.
Takeaway
QiSampler helps scientists figure out which scoring methods work best for analyzing large sets of biological data.
Methodology
QiSampler uses a repetitive sampling strategy to evaluate scoring schemes against a gold standard dataset.
Potential Biases
The algorithm may be biased if a few true positives have significantly higher scores than others.
Limitations
QiSampler may struggle with datasets that have very few known cases or limited score levels.
Digital Object Identifier (DOI)
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