| Configuration: CI-SpliceAI (Fortier et al. 2026) | 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) | 0.89 Balanced accuracy (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCI-SpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'CI-SpliceAI', column 'Balanced Accuracy' |
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| Configuration: CI-SpliceAI (Fortier et al. 2026) | 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) | 0.886 F1 (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCI-SpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'CI-SpliceAI', column 'F1' |
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| Configuration: CI-SpliceAI (Fortier et al. 2026) | 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) | 0.851 Recall (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCI-SpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'CI-SpliceAI', column 'Sensitivity' |
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| Configuration: CI-SpliceAI (Fortier et al. 2026) | 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) | 0.93 Specificity (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCI-SpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'CI-SpliceAI', column 'Specificity' |
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| Configuration: Legacy Ensemble (Fortier et al. 2026) | 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) | 0.785 Balanced accuracy (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLegacy Ensemble on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'Legacy Ensemble', column 'Balanced Accuracy' |
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| Configuration: Legacy Ensemble (Fortier et al. 2026) | 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) | 0.778 F1 (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLegacy Ensemble on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'Legacy Ensemble', column 'F1' |
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| Configuration: Legacy Ensemble (Fortier et al. 2026) | 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) | 0.752 Recall (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLegacy Ensemble on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'Legacy Ensemble', column 'Sensitivity' |
|---|
| Configuration: Legacy Ensemble (Fortier et al. 2026) | 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) | 0.818 Specificity (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceLegacy Ensemble on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'Legacy Ensemble', column 'Specificity' |
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| Configuration: OpenSpliceAI (Fortier et al. 2026) | 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) | 0.841 Balanced accuracy (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceOpenSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'OpenSpliceAI', column 'Balanced Accuracy' |
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| Configuration: OpenSpliceAI (Fortier et al. 2026) | 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) | 0.815 F1 (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceOpenSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'OpenSpliceAI', column 'F1' |
|---|
| Configuration: OpenSpliceAI (Fortier et al. 2026) | 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) | 0.702 Recall (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceOpenSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'OpenSpliceAI', column 'Sensitivity' |
|---|
| Configuration: OpenSpliceAI (Fortier et al. 2026) | 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) | 0.979 Specificity (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceOpenSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'OpenSpliceAI', column 'Specificity' |
|---|
| Configuration: SpliceAI (Fortier et al. 2026) | 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) | 0.94 Balanced accuracy (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'SpliceAI', column 'Balanced Accuracy' |
|---|
| Configuration: SpliceAI (Fortier et al. 2026) | 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) | 0.939 F1 (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'SpliceAI', column 'F1' |
|---|
| Configuration: SpliceAI (Fortier et al. 2026) | 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) | 0.93 Recall (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'SpliceAI', column 'Sensitivity' |
|---|
| Configuration: SpliceAI (Fortier et al. 2026) | 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) | 0.95 Specificity (threshold optimised per algorithm for balanced accuracy on this dataset) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceSpliceAI on the Barbosa et al. deep intronic pathogenic variants with matched common controls (484 variants) rna-splicing-20261011-fortier2026-protocol-barbosa-deep-intronic Aggregation: Not reported Analyzing the performance of deep learning splice prediction algorithms (Fortier, Rudy and Scherer, PLOS One 2026) · Table 4, row 'SpliceAI', column 'Specificity' |
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