| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 59% accuracy percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'Accuracy (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 9 false-negative-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'FN' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 24 false-positive-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'FP' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 0.15 matthews-correlation-coefficient unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'MCC' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 0 count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'Missing values' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 80% negative-predictive-value percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'NPV (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 33% precision percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'PPV (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 57% recall percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'Sensitivity (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 60% specificity percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'Specificity (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 36 true-negative-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'TN' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 deep-intronic variants: classification against mini- or midigene splicing results (Riepe et al. Table 4)
Dataset: ABCA4 deep-intronic variants (Riepe et al. benchmark set) | 12 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 deep-intronic variants splicing-follow-up-20261009-protocol-riepe2021-abca4-di Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 4 row 'CADD', column 'TP' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 62% accuracy percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'Accuracy (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 24 false-negative-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'FN' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 3 false-positive-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'FP' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 0.12 matthews-correlation-coefficient unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'MCC' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 0 count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'Missing values' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 14% negative-predictive-value percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'NPV (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 93% precision percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'PPV (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 63% recall percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'Sensitivity (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 57% specificity percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'Specificity (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 4 true-negative-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'TN' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: ABCA4 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 3)
Dataset: ABCA4 noncanonical splice-site variants (Riepe et al. benchmark set) | 40 true-positive-count count · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on ABCA4 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-abca4-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 3 row 'CADD', column 'TP' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: MYBPC3 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 5)
Dataset: MYBPC3 noncanonical splice-site variants (Riepe et al. benchmark set) | 66% accuracy percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on MYBPC3 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-mybpc3-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 5 row 'CADD', column 'Accuracy (%)' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: MYBPC3 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 5)
Dataset: MYBPC3 noncanonical splice-site variants (Riepe et al. benchmark set) | 13 false-negative-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on MYBPC3 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-mybpc3-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 5 row 'CADD', column 'FN' |
|---|
| Configuration: CADD (Riepe et al. 2021) | Protocol: MYBPC3 noncanonical splice-site variants: classification against mini- or midigene splicing results (Riepe et al. Table 5)
Dataset: MYBPC3 noncanonical splice-site variants (Riepe et al. benchmark set) | 8 false-positive-count count · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on MYBPC3 noncanonical splice-site variants splicing-follow-up-20261009-protocol-riepe2021-mybpc3-ncss Aggregation: Not reported Benchmarking deep learning splice prediction tools using functional splice assays · Table 5 row 'CADD', column 'FP' |
|---|