Configuration

CanDrA (Chen et al. 2020)

Configuration of CanDrA: Support vector machine; features: Structural, evolutionary, and genomic features, published predictors.

4 evaluations · 20 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

4 evaluations · 20 results. Different protocols are not a single leaderboard.

Sorted by Accuracy (cell viability drivers versus neutral, default categorical calls) (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	Protocol and dataset	Accuracy (cell viability drivers versus neutral, default categorical calls)	Accuracy (cell viability drivers versus neutral, median-score threshold)	Accuracy (OncoKB benchmark (positive set not stated), default categorical calls)	Accuracy (OncoKB benchmark (positive set not stated), median-score threshold)	Negative predictive value (cell viability drivers versus neutral, default categorical calls)	Negative predictive value (cell viability drivers versus neutral, median-score threshold)	Negative predictive value (OncoKB benchmark (positive set not stated), default categorical calls)	Negative predictive value (OncoKB benchmark (positive set not stated), median-score threshold)	Precision (cell viability drivers versus neutral, default categorical calls)	Precision (cell viability drivers versus neutral, median-score threshold)	Precision (OncoKB benchmark (positive set not stated), default categorical calls)	Precision (OncoKB benchmark (positive set not stated), median-score threshold)	Recall (cell viability drivers versus neutral, default categorical calls)	Recall (cell viability drivers versus neutral, median-score threshold)	Recall (OncoKB benchmark (positive set not stated), default categorical calls)	Recall (OncoKB benchmark (positive set not stated), median-score threshold)	Specificity (cell viability drivers versus neutral, default categorical calls)	Specificity (cell viability drivers versus neutral, median-score threshold)	Specificity (OncoKB benchmark (positive set not stated), default categorical calls)	Specificity (OncoKB benchmark (positive set not stated), median-score threshold)
CanDrA (Chen et al. 2020)Evaluation	Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22), Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)	0.59	Not reported	Not reported	Not reported	0.69	Not reported	Not reported	Not reported	0.56	Not reported	Not reported	Not reported	0.87	Not reported	Not reported	Not reported	0.3	Not reported	Not reported	Not reported
CanDrA (Chen et al. 2020)Evaluation	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10), OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	Not reported	Not reported	0.49	Not reported	Not reported	Not reported	0.46	Not reported	Not reported	Not reported	0.49	Not reported	Not reported	Not reported	0.8	Not reported	Not reported	Not reported	0.17	Not reported
CanDrA (Chen et al. 2020)Evaluation	OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9), OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	Not reported	Not reported	Not reported	0.59	Not reported	Not reported	Not reported	0.59	Not reported	Not reported	Not reported	0.59	Not reported	Not reported	Not reported	0.59	Not reported	Not reported	Not reported	0.59
CanDrA (Chen et al. 2020)Evaluation	Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21), Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)	Not reported	0.67	Not reported	Not reported	Not reported	0.67	Not reported	Not reported	Not reported	0.67	Not reported	Not reported	Not reported	0.67	Not reported	Not reported	Not reported	0.67	Not reported	Not reported

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.49 Accuracy (OncoKB benchmark (positive set not stated), default categorical calls) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) · Additional file 10 (sheet 'Additional_file_10'), B18; Algorithm 'CanDrA'; column 'Accuracy (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.46 Negative predictive value (OncoKB benchmark (positive set not stated), default categorical calls) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) · Additional file 10 (sheet 'Additional_file_10'), F18; Algorithm 'CanDrA'; column 'NPV (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.49 Precision (OncoKB benchmark (positive set not stated), default categorical calls) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) · Additional file 10 (sheet 'Additional_file_10'), E18; Algorithm 'CanDrA'; column 'PPV (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.8 Recall (OncoKB benchmark (positive set not stated), default categorical calls) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) · Additional file 10 (sheet 'Additional_file_10'), C18; Algorithm 'CanDrA'; column 'Sensitivity (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.17 Specificity (OncoKB benchmark (positive set not stated), default categorical calls) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-default Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) · Additional file 10 (sheet 'Additional_file_10'), D18; Algorithm 'CanDrA'; column 'Specificity (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.59 Accuracy (OncoKB benchmark (positive set not stated), median-score threshold) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) · Additional file 9 (sheet 'Additional_file_9'), B26; Algorithm 'CanDrA'; column 'Accuracy (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.59 Negative predictive value (OncoKB benchmark (positive set not stated), median-score threshold) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) · Additional file 9 (sheet 'Additional_file_9'), F26; Algorithm 'CanDrA'; column 'NPV (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.59 Precision (OncoKB benchmark (positive set not stated), median-score threshold) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) · Additional file 9 (sheet 'Additional_file_9'), E26; Algorithm 'CanDrA'; column 'PPV (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.59 Recall (OncoKB benchmark (positive set not stated), median-score threshold) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) · Additional file 9 (sheet 'Additional_file_9'), C26; Algorithm 'CanDrA'; column 'Sensitivity (±2σ)'
Configuration: CanDrA (Chen et al. 2020)	Protocol: OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) Dataset: OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)	0.59 Specificity (OncoKB benchmark (positive set not stated), median-score threshold) 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 checked Methods, coverage and source CanDrA on OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020) somatic-oncogenicity-20261009-protocol-chen2020-oncokb-median Aggregation: Not reported Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) · Additional file 9 (sheet 'Additional_file_9'), D26; Algorithm 'CanDrA'; 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 (cell viability drivers versus neutral, default categorical calls) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, default categorical calls) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, default categorical calls) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, default categorical calls) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, default categorical calls) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, median-score threshold) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, median-score threshold) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, median-score threshold) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, median-score threshold) 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 checked Methods, coverage and source CanDrA 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 (cell viability drivers versus neutral, median-score threshold) 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 checked Methods, coverage and source CanDrA 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)'

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Comprehensive assessment of computational algorithms in predicting cancer driver mutations · Original source · Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML

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Stable ID: somatic-oncogenicity-20261009-config-chen2020-candra

areas: dna-genomes
contexts: clinical_research
method types: supervised_machine_learning
reported name: CanDrA
version: Scores from CanDrA download (MD Anderson)
foundation model eligible: false
protocol: Support vector machine; features: Structural, evolutionary, and genomic features, published predictors
source locator: Table 1; Methods 'Inter-correlation analysis among algorithms'
configuration: default positive category: Driver; default negative category: Passenger
provenance note: CanDrA scores as used in the cited comparison.

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