Benchmark 5 truth set in Chen et al. 2020.
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50 evaluations · 250 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: CADD (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.59 accuracy fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), B23; Algorithm 'CADD'; column 'Accuracy (±2σ)' |
| Configuration: CADD (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.59 negative-predictive-value fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), F23; Algorithm 'CADD'; column 'NPV (±2σ)' |
| Configuration: CADD (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.59 precision fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), E23; Algorithm 'CADD'; column 'PPV (±2σ)' |
| Configuration: CADD (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.59 recall fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), C23; Algorithm 'CADD'; column 'Sensitivity (±2σ)' |
| Configuration: CADD (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.59 specificity fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCADD on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), D23; Algorithm 'CADD'; column 'Specificity (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.59 accuracy fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) · Additional file 22 (sheet 'Additional_file_22'), B7; Algorithm 'CanDrA'; column 'Accuracy (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.69 negative-predictive-value fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) · Additional file 22 (sheet 'Additional_file_22'), F7; Algorithm 'CanDrA'; column 'NPV (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.56 precision fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) · Additional file 22 (sheet 'Additional_file_22'), E7; Algorithm 'CanDrA'; column 'PPV (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.87 recall fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) · Additional file 22 (sheet 'Additional_file_22'), C7; Algorithm 'CanDrA'; column 'Sensitivity (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.3 specificity fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) · Additional file 22 (sheet 'Additional_file_22'), D7; Algorithm 'CanDrA'; column 'Specificity (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.67 accuracy fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), B5; Algorithm 'CanDrA'; column 'Accuracy (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.67 negative-predictive-value fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), F5; Algorithm 'CanDrA'; column 'NPV (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.67 precision fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), E5; Algorithm 'CanDrA'; column 'PPV (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.67 recall fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), C5; Algorithm 'CanDrA'; column 'Sensitivity (±2σ)' |
| Configuration: CanDrA (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.67 specificity fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCanDrA on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), D5; Algorithm 'CanDrA'; column 'Specificity (±2σ)' |
| Configuration: CHASM (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.69 accuracy fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCHASM on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), B4; Algorithm 'CHASM'; column 'Accuracy (±2σ)' |
| Configuration: CHASM (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.69 negative-predictive-value fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCHASM on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), F4; Algorithm 'CHASM'; column 'NPV (±2σ)' |
| Configuration: CHASM (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.69 precision fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCHASM on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), E4; Algorithm 'CHASM'; column 'PPV (±2σ)' |
| Configuration: CHASM (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.69 recall fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCHASM on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), C4; Algorithm 'CHASM'; column 'Sensitivity (±2σ)' |
| Configuration: CHASM (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.69 specificity fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCHASM on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), D4; Algorithm 'CHASM'; column 'Specificity (±2σ)' |
| Configuration: CTAT-cancer (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.7 accuracy fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCTAT-cancer on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), B2; Algorithm 'CTAT-cancer'; column 'Accuracy (±2σ)' |
| Configuration: CTAT-cancer (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.7 negative-predictive-value fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCTAT-cancer on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), F2; Algorithm 'CTAT-cancer'; column 'NPV (±2σ)' |
| Configuration: CTAT-cancer (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.7 precision fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCTAT-cancer on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), E2; Algorithm 'CTAT-cancer'; column 'PPV (±2σ)' |
| Configuration: CTAT-cancer (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.7 recall fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCTAT-cancer on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), C2; Algorithm 'CTAT-cancer'; column 'Sensitivity (±2σ)' |
| Configuration: CTAT-cancer (Chen et al. 2020) | Protocol: Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) Dataset: Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) | 0.7 specificity fraction · higher Uncertainty: Not yet extracted: Printed as 'mean (lower-upper)' under the column header '(±2σ)': per Methods, the mean and two standard deviations over 100 random draws. That is not a confidence interval, and the schema's standard_deviation type would need a standard deviation derived from rounded bounds, so no structured uncertainty is recorded. The printed range is kept in printed_source_cell; every range is symmetric about the mean to rounding. Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceCTAT-cancer on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-viability-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) · Additional file 21 (sheet 'Additional_file_21'), D2; Algorithm 'CTAT-cancer'; column 'Specificity (±2σ)' |
Source checking is not independent reproduction. Release 2026-10-10-7fcc3e48a123.
A dataset supplies biological observations. The evaluation protocol defines how those observations are split, used and scored.
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
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5 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.population 797 published and 164 new missense mutations; positives activating, inactivating, inhibitory or non-inhibitory in either cell model; negatives neutral in both Context-only references | Comprehensive assessment of computational algorithms in predicting cancer driver mutations Results 'Benchmark 5'; Methods 'In vitro cell viability assay benchmark' Version: Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.source_locator Results 'Benchmark 5'; Methods 'In vitro cell viability assay benchmark' Context-only references | Comprehensive assessment of computational algorithms in predicting cancer driver mutations Results 'Benchmark 5'; Methods 'In vitro cell viability assay benchmark' Version: Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.split 400 positives and 400 negatives drawn at random, 100 times, for the binary metrics Context-only references | Comprehensive assessment of computational algorithms in predicting cancer driver mutations Results 'Benchmark 5'; Methods 'In vitro cell viability assay benchmark' Version: Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| description Benchmark 5 truth set in Chen et al. 2020. Context-only references | Comprehensive assessment of computational algorithms in predicting cancer driver mutations Results 'Benchmark 5'; Methods 'In vitro cell viability assay benchmark' Version: Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new) Context-only references | Comprehensive assessment of computational algorithms in predicting cancer driver mutations Results 'Benchmark 5'; Methods 'In vitro cell viability assay benchmark' Version: Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-10-7fcc3e48a123 · Record review: source checked
Stable ID: somatic-oncogenicity-20261009-data-chen2020-cell-viability