Configuration

PROVEAN (Chen et al. 2020)

Configuration of PROVEAN: Scores from dbNSFP v4.0; Delta alignment score; features: Sequence homology.

4 evaluations · 20 results 1 open disagreement between studies involves its model

Overview

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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)
PROVEAN (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)	0.62	Not reported	Not reported	Not reported	0.7	Not reported	Not reported	Not reported	0.57	Not reported	Not reported	Not reported	0.78	Not reported	Not reported	Not reported	0.48	Not reported	Not reported	Not reported
PROVEAN (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	0.67	Not reported	Not reported	Not reported	0.8	Not reported	Not reported	Not reported	0.61	Not reported	Not reported	Not reported	0.89	Not reported	Not reported	Not reported	0.45	Not reported
PROVEAN (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	0.69	Not reported	Not reported	Not reported	0.7	Not reported	Not reported	Not reported	0.69	Not reported	Not reported	Not reported	0.7	Not reported	Not reported	Not reported	0.68
PROVEAN (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	0.59	Not reported	Not reported	Not reported	0.59	Not reported	Not reported	Not reported	0.58	Not reported	Not reported	Not reported	0.59	Not reported	Not reported	Not reported	0.58	Not reported	Not reported

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: PROVEAN (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)	0.67 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), B3; Algorithm 'PROVEAN'; column 'Accuracy (±2σ)'
Configuration: PROVEAN (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 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), F3; Algorithm 'PROVEAN'; column 'NPV (±2σ)'
Configuration: PROVEAN (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)	0.61 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), E3; Algorithm 'PROVEAN'; column 'PPV (±2σ)'
Configuration: PROVEAN (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)	0.89 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), C3; Algorithm 'PROVEAN'; column 'Sensitivity (±2σ)'
Configuration: PROVEAN (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)	0.45 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), D3; Algorithm 'PROVEAN'; column 'Specificity (±2σ)'
Configuration: PROVEAN (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)	0.69 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), B2; Algorithm 'PROVEAN'; column 'Accuracy (±2σ)'
Configuration: PROVEAN (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)	0.7 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), F2; Algorithm 'PROVEAN'; column 'NPV (±2σ)'
Configuration: PROVEAN (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)	0.69 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), E2; Algorithm 'PROVEAN'; column 'PPV (±2σ)'
Configuration: PROVEAN (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)	0.7 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), C2; Algorithm 'PROVEAN'; column 'Sensitivity (±2σ)'
Configuration: PROVEAN (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)	0.68 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), D2; Algorithm 'PROVEAN'; column 'Specificity (±2σ)'
Configuration: PROVEAN (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)	0.62 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), B3; Algorithm 'PROVEAN'; column 'Accuracy (±2σ)'
Configuration: PROVEAN (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)	0.7 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), F3; Algorithm 'PROVEAN'; column 'NPV (±2σ)'
Configuration: PROVEAN (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)	0.57 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), E3; Algorithm 'PROVEAN'; column 'PPV (±2σ)'
Configuration: PROVEAN (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)	0.78 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), C3; Algorithm 'PROVEAN'; column 'Sensitivity (±2σ)'
Configuration: PROVEAN (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)	0.48 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), D3; Algorithm 'PROVEAN'; column 'Specificity (±2σ)'
Configuration: PROVEAN (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 (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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), B25; Algorithm 'PROVEAN'; column 'Accuracy (±2σ)'
Configuration: PROVEAN (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 (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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), F25; Algorithm 'PROVEAN'; column 'NPV (±2σ)'
Configuration: PROVEAN (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)	0.58 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), E25; Algorithm 'PROVEAN'; column 'PPV (±2σ)'
Configuration: PROVEAN (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 (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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), C25; Algorithm 'PROVEAN'; column 'Sensitivity (±2σ)'
Configuration: PROVEAN (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)	0.58 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	Independent external evaluation · Source checked Methods, coverage and source PROVEAN 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'), D25; Algorithm 'PROVEAN'; column 'Specificity (±2σ)'

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Claims, original sources and review scope · Release 2026-11-02-a7f03c9e55a5
Property and statement	Original source and location	Review and provenance
Facets: method types 1 values conventional_pipeline Individual claims	Comprehensive assessment of computational algorithms in predicting cancer driver mutations Original source ↗ Chen et al. 2020 Table 1: PROVEAN uses a delta alignment score Version: Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML Retrieved: 2026-10-09T20:45:50Z	conflicting claim ["source-hash-verification","automated-source-review"] · 2026-10-12 Audit details Correct the method type from specialist to conventional_pipeline (use-case baseline review 2026-10-12, table 2). Named in the SIFT row's evidence ("The same applies to PROVEAN") rather than in its record IDs. Evidence from docs/reviews/use-cases/baselines-2026-10-12.md. Independent review 2026-10-12: approved. See docs/reviews/2026-10-12-method-type-corrections.md. Conflicting source claim: `"[\"conventional_pipeline\"]"`. Claim record status: source_checked. Field: `facets.method_types` Claim: metadata-correction-tags-20261012-75e52e0a261775fc84f8 Source artifact SHA-256: `fef52f70c3a0ff3f82902f87080933c220e12274787de6309b41ee283daf8b8e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact Extraction artifact SHA-256: `fef52f70c3a0ff3f82902f87080933c220e12274787de6309b41ee283daf8b8e` Extraction artifact

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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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Technical metadata and extraction receipts

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

areas: dna-genomes
contexts: clinical_research
method types: conventional_pipeline
reported name: PROVEAN
version: Scores from dbNSFP v4.0
foundation model eligible: false
protocol: Delta alignment score; features: Sequence homology
source locator: Table 1; Methods 'Inter-correlation analysis among algorithms'
configuration: default positive category: Deleterious; default negative category: Neutral
provenance note: PROVEAN scores as used in the cited comparison.

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