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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.10262 |
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| _version_ | 1866910743943708672 |
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| author | Zhang, Cindy Roth, Frederick P. |
| author_facet | Zhang, Cindy Roth, Frederick P. |
| contents | Computational variant effect predictors (VEPs) are providing increasingly strong evidence to classify the pathogenicity of missense variants. Precision vs. recall analysis is useful in evaluating VEP performance, especially when adjusted for imbalanced test sets. Here, we describe VEPerform, a web-based tool for evaluating the performance of VEPs at the gene level using balanced precision vs. recall curve (BPRC) analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10262 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VEPerform: a web resource for evaluating the performance of variant effect predictors Zhang, Cindy Roth, Frederick P. Genomics Computational variant effect predictors (VEPs) are providing increasingly strong evidence to classify the pathogenicity of missense variants. Precision vs. recall analysis is useful in evaluating VEP performance, especially when adjusted for imbalanced test sets. Here, we describe VEPerform, a web-based tool for evaluating the performance of VEPs at the gene level using balanced precision vs. recall curve (BPRC) analysis. |
| title | VEPerform: a web resource for evaluating the performance of variant effect predictors |
| topic | Genomics |
| url | https://arxiv.org/abs/2412.10262 |