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1 evaluation · 4 results. Different protocols are not a single leaderboard.
Protocol: Splice-altering variant classification, Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) (Fortier et al. Table 4) · Dataset: Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants)
Sorted by Balanced accuracy (threshold optimised per algorithm for balanced accuracy on this dataset) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.
| Tested configuration | Balanced accuracy (threshold optimised per algorithm for balanced accuracy on this dataset) | F1 (threshold optimised per algorithm for balanced accuracy on this dataset) | Recall (threshold optimised per algorithm for balanced accuracy on this dataset) | Specificity (threshold optimised per algorithm for balanced accuracy on this dataset) |
|---|---|---|---|---|
| SpliceAI (Fortier et al. 2026)Evaluation | 0.94 | 0.939 | 0.93 | 0.95 |
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18 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Comparison: adaptation threshold optimised per algorithm for balanced accuracy on this dataset Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: aggregation Whole dataset Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: budget Not reported Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: dataset version As assembled by Fortier et al. 2026 Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: inputs Variant with genomic sequence context (VCF) Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: metric implementation Sensitivity, specificity, F1 = 2TP/(2TP+FP+FN), balanced accuracy = mean of sensitivity and specificity (Equations 1 to 4) Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: population 242 pathogenic intronic variants more than 10 bp from the nearest splice site with experimental evidence of aberrant splicing (RT-PCR, cDNA sequencing, RNA-seq or minigene), 194 causing pseudoexon activation; 242 matched common gnomAD v2.1 intronic negatives (AF > 1%) from the same genes, absent from ClinVar Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: protocol id rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Comparison: split Whole set Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Origin independent_paper Context-only references | Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) Table 4 row 'SpliceAI' Version: PLOS One 21(5):e0348885, published 2026-05-13; PMC13170886 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
Stable ID: rna-splicing-20261011-fortier2026-eval-barbosa-deep-intronic-spliceai