Configuration

AnnotSV (Zhang et al. 2026)

Configuration of AnnotSV: version v3.5.2; ACMG rule-based scoring; run from GitHub or the official web interface per the original instructions; input CNV coordinates lifted from hg19 to hg38.

3 evaluations · 13 results

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Evaluations and results

3 evaluations · 13 results. Different protocols are not a single leaderboard.

Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)

Sorted by Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') (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	Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy')	Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy')	Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy')	Accuracy (ACMG five-category classification; averaging across classes not stated)	F1 (ACMG five-category classification; averaging across classes not stated)	False positive rate (ACMG five-category classification; averaging across classes not stated)	MCC (ACMG five-category classification; averaging across classes not stated)	Precision (ACMG five-category classification; averaging across classes not stated)	Recall (ACMG five-category classification; averaging across classes not stated)	Specificity (ACMG five-category classification; averaging across classes not stated)
AnnotSV (Zhang et al. 2026)Evaluation	Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL)	75.2% (best in column)	95.2% (best in column)	42.9%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
AnnotSV (Zhang et al. 2026)Evaluation	Three-category CNV classification, per-class agreement on duplications (Zhang et al. Table 4, DUP)	49.3%	86.4%	67.5% (best in column)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
AnnotSV (Zhang et al. 2026)Evaluation	ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3)	Not reported	Not reported	Not reported	60.2%	38.1%	11.2%	0.43	40%	43.5%	88.8%

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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: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	60.2% Accuracy (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'Accuracy (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	38.1% F1 (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'F1(%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	11.2% False positive rate (ACMG five-category classification; averaging across classes not stated) percent · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'FPR*(%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	0.43 MCC (ACMG five-category classification; averaging across classes not stated) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'MCC*'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	40% Precision (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'PPV*(%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	43.5% Recall (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'Sensitivity (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	88.8% Specificity (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'Specificity (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	75.2% Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 4, block 'DEL', row 'B', column 'AnnotSV (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	95.2% Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 4, block 'DEL', row 'P', column 'AnnotSV (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	42.9% Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 4, block 'DEL', row 'VUS', column 'AnnotSV (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on duplications (Zhang et al. Table 4, DUP) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	49.3% Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (three category duplications) cnv-20261011-zhang2026-protocol-three-category-duplications Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 4, block 'DUP', row 'B', column 'AnnotSV (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on duplications (Zhang et al. Table 4, DUP) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	86.4% Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (three category duplications) cnv-20261011-zhang2026-protocol-three-category-duplications Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 4, block 'DUP', row 'P', column 'AnnotSV (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on duplications (Zhang et al. Table 4, DUP) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	67.5% Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (three category duplications) cnv-20261011-zhang2026-protocol-three-category-duplications Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 4, block 'DUP', row 'VUS', column 'AnnotSV (%)'

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Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Original source · Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML

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Technical metadata and extraction receipts

Stable ID: cnv-20261011-zhang2026-config-annotsv

areas: dna-genomes
contexts: clinical_research
method types: conventional_pipeline
reported name: AnnotSV
foundation model eligible: false
parameters: ACMG rule-based scoring; run from GitHub or the official web interface per the original instructions; input CNV coordinates lifted from hg19 to hg38
access reuse: Obtained from GitHub or the official web interface (Methods 'Model operation and results collection')
source locator: Table 1; Methods 'Model operation and results collection'
version: v3.5.2
provenance note: AnnotSV as run by Zhang et al. 2026.

Related records

configuration of: AnnotSV
system: AnnotSV on the expert-classified ACMG CNV benchmark (five category)
system: AnnotSV on the expert-classified ACMG CNV benchmark (three category deletions)
system: AnnotSV on the expert-classified ACMG CNV benchmark (three category duplications)

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