VEPerform: a web resource for evaluating the performance of variant effect predictors
2024
VEPerform: A Tool for Evaluating Variant Effect Predictors
publication
Author Information
Author(s): Zhang Cindy, Roth Frederick P.
Primary Institution: Cornell University
Hypothesis
The study aims to evaluate the performance of computational variant effect predictors (VEPs) using a new web-based tool.
Conclusion
VEPerform provides a method for assessing the performance of VEPs at the gene level.
Supporting Evidence
- Computational variant effect predictors are increasingly strong in classifying the pathogenicity of missense variants.
- Precision vs. recall analysis is useful for evaluating VEP performance.
Takeaway
This study introduces a new online tool that helps scientists check how well certain genetic tests work.
Methodology
The study describes the use of balanced precision vs. recall curve (BPRC) analysis for evaluating VEP performance.
Digital Object Identifier (DOI)
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