Polyphen2_HVAR (Chen et al. 2020)
Polyphen2_HVAR scores as used in the cited comparison.
Overview
Polyphen2_HVAR scores as used in the cited comparison.
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.
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Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: Polyphen2_HVAR (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) | 0.64 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 sourcesomatic-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'), B7; Algorithm 'Polyphen2_HVAR'; column 'Accuracy (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.76 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 sourcesomatic-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'), F7; Algorithm 'Polyphen2_HVAR'; column 'NPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.6 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 sourcesomatic-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'), E7; Algorithm 'Polyphen2_HVAR'; column 'PPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.88 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 sourcesomatic-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'), C7; Algorithm 'Polyphen2_HVAR'; column 'Sensitivity (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.39 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 sourcesomatic-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'), D7; Algorithm 'Polyphen2_HVAR'; column 'Specificity (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.62 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 sourcesomatic-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'), B22; Algorithm 'Polyphen2_HVAR'; column 'Accuracy (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.62 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 sourcesomatic-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'), F22; Algorithm 'Polyphen2_HVAR'; column 'NPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.62 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 sourcesomatic-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'), E22; Algorithm 'Polyphen2_HVAR'; column 'PPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.63 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 sourcesomatic-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'), C22; Algorithm 'Polyphen2_HVAR'; column 'Sensitivity (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.61 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 sourcesomatic-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'), D22; Algorithm 'Polyphen2_HVAR'; column 'Specificity (±2σ)' |
| Configuration: Polyphen2_HVAR (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 | Independent external evaluation · Source checkedMethods, coverage and sourcesomatic-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'), B6; Algorithm 'Polyphen2_HVAR'; column 'Accuracy (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.65 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 sourcesomatic-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'), F6; Algorithm 'Polyphen2_HVAR'; column 'NPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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 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 sourcesomatic-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'), E6; Algorithm 'Polyphen2_HVAR'; column 'PPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.8 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 sourcesomatic-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'), C6; Algorithm 'Polyphen2_HVAR'; column 'Sensitivity (±2σ)' |
| Configuration: Polyphen2_HVAR (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) | 0.38 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 sourcesomatic-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'), D6; Algorithm 'Polyphen2_HVAR'; column 'Specificity (±2σ)' |
| Configuration: Polyphen2_HVAR (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 sourcePolyphen2_HVAR 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'), B24; Algorithm 'Polyphen2_HVAR'; column 'Accuracy (±2σ)' |
| Configuration: Polyphen2_HVAR (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 sourcePolyphen2_HVAR 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'), F24; Algorithm 'Polyphen2_HVAR'; column 'NPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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 sourcePolyphen2_HVAR 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'), E24; Algorithm 'Polyphen2_HVAR'; column 'PPV (±2σ)' |
| Configuration: Polyphen2_HVAR (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 sourcePolyphen2_HVAR 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'), C24; Algorithm 'Polyphen2_HVAR'; column 'Sensitivity (±2σ)' |
| Configuration: Polyphen2_HVAR (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 sourcePolyphen2_HVAR 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'), D24; Algorithm 'Polyphen2_HVAR'; column 'Specificity (±2σ)' |
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Sources and history
Release 2026-10-10-7fcc3e48a123 · Record review: source checked
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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
Technical metadata and extraction receipts
Stable ID: somatic-oncogenicity-20261009-config-chen2020-polyphen2-hvar
- areas
- dna-genomes
- contexts
- clinical_research
- method types
- supervised_machine_learning
- reported name
- Polyphen2_HVAR
- version
- Scores from dbNSFP v4.0
- foundation model eligible
- false
- protocol
- Naïve Bayes classifier; features: Eight sequence-based and three structure-based predictive features
- source locator
- Table 1; Methods 'Inter-correlation analysis among algorithms'
- configuration
- default positive category: Probably damaging; Possibly damaging; default negative category: Benign
Related records
- configuration of: PolyPhen-2
- system: Polyphen2_HVAR on OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020)
- system: Polyphen2_HVAR on OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020)
- system: Polyphen2_HVAR on cell viability drivers versus neutral, default categorical calls (Chen et al. 2020)
- system: Polyphen2_HVAR on cell viability drivers versus neutral, median-score threshold (Chen et al. 2020)