Configuration

UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)

Configuration as run in the cited comparison.

Overview

Configuration as run 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 · 24 results. Different protocols are not a single leaderboard.

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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: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction 0 to 0.03 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.686 accuracy fraction · higher Uncertainty: 95% CI 0.677782380024197 to 0.693932114531523. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction 0 to 0.03 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-0-3pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 25 (feat 'CNV', ichorcna_strat '[0, 0.03]', .metric 'Accuracy'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction 0 to 0.03 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.814 auprc unitless · higher Uncertainty: 95% CI 0.807254669078835 to 0.820408000255825. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction 0 to 0.03 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-0-3pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 21 (feat 'CNV', ichorcna_strat '[0, 0.03]', .metric 'AUPRC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction 0 to 0.03 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.736 auroc unitless · higher Uncertainty: 95% CI 0.728041101052221 to 0.743951492070195. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction 0 to 0.03 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-0-3pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 20 (feat 'CNV', ichorcna_strat '[0, 0.03]', .metric 'AUROC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction 0 to 0.03 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.744 f1-score fraction · higher Uncertainty: 95% CI 0.735562771635316 to 0.752181871749292. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction 0 to 0.03 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-0-3pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 24 (feat 'CNV', ichorcna_strat '[0, 0.03]', .metric 'F1'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction 0 to 0.03 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.735 recall fraction · higher Uncertainty: 95% CI 0.721953323221686 to 0.745661552818609. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction 0 to 0.03 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-0-3pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 22 (feat 'CNV', ichorcna_strat '[0, 0.03]', .metric 'Sensitivity'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction 0 to 0.03 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.605 specificity fraction · higher Uncertainty: 95% CI 0.591140127956712 to 0.619777095106214. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction 0 to 0.03 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-0-3pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 23 (feat 'CNV', ichorcna_strat '[0, 0.03]', .metric 'Specificity'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.875 accuracy fraction · higher Uncertainty: 95% CI 0.868668219763153 to 0.881448456610367. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 61 (feat 'CNV', ichorcna_strat '(0.03, 0.1]', .metric 'Accuracy'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.914 auprc unitless · higher Uncertainty: 95% CI 0.908229150984468 to 0.918791814512549. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 57 (feat 'CNV', ichorcna_strat '(0.03, 0.1]', .metric 'AUPRC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.926 auroc unitless · higher Uncertainty: 95% CI 0.919865039293221 to 0.931556862075475. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 56 (feat 'CNV', ichorcna_strat '(0.03, 0.1]', .metric 'AUROC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.81 f1-score fraction · higher Uncertainty: 95% CI 0.800700064201192 to 0.820051042068867. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 60 (feat 'CNV', ichorcna_strat '(0.03, 0.1]', .metric 'F1'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.748 recall fraction · higher Uncertainty: 95% CI 0.734597960409929 to 0.760801330723526. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 58 (feat 'CNV', ichorcna_strat '(0.03, 0.1]', .metric 'Sensitivity'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.946 specificity fraction · higher Uncertainty: 95% CI 0.938072044858814 to 0.952421746765391. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 59 (feat 'CNV', ichorcna_strat '(0.03, 0.1]', .metric 'Specificity'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with all tumour fractions vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.739 accuracy fraction · higher Uncertainty: 95% CI 0.732531455636329 to 0.745603845619276. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), all tumour fractions ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-all Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 133 (feat 'CNV', ichorcna_strat 'all', .metric 'Accuracy'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with all tumour fractions vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.933 auprc unitless · higher Uncertainty: 95% CI 0.930766088311422 to 0.935304298039049. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), all tumour fractions ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-all Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 129 (feat 'CNV', ichorcna_strat 'all', .metric 'AUPRC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with all tumour fractions vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.817 auroc unitless · higher Uncertainty: 95% CI 0.811816023264348 to 0.822813746781197. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), all tumour fractions ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-all Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 128 (feat 'CNV', ichorcna_strat 'all', .metric 'AUROC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with all tumour fractions vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.807 f1-score fraction · higher Uncertainty: 95% CI 0.801436970141717 to 0.81336522207616. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), all tumour fractions ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-all Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 132 (feat 'CNV', ichorcna_strat 'all', .metric 'F1'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with all tumour fractions vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.745 recall fraction · higher Uncertainty: 95% CI 0.732901131538364 to 0.756157326380321. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), all tumour fractions ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-all Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 130 (feat 'CNV', ichorcna_strat 'all', .metric 'Sensitivity'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with all tumour fractions vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.724 specificity fraction · higher Uncertainty: 95% CI 0.699821921055358 to 0.74667535310548. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), all tumour fractions ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-all Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 131 (feat 'CNV', ichorcna_strat 'all', .metric 'Specificity'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.982 accuracy fraction · higher Uncertainty: 95% CI 0.978649712567612 to 0.98557813709692. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-over-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 97 (feat 'CNV', ichorcna_strat '(0.1, 1]', .metric 'Accuracy'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.997 auprc unitless · higher Uncertainty: 95% CI 0.996562977107993 to 0.998385954076608. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-over-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 93 (feat 'CNV', ichorcna_strat '(0.1, 1]', .metric 'AUPRC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.998 auroc unitless · higher Uncertainty: 95% CI 0.99750212941639 to 0.998901652002618. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-over-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 92 (feat 'CNV', ichorcna_strat '(0.1, 1]', .metric 'AUROC'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.976 f1-score fraction · higher Uncertainty: 95% CI 0.971367625741786 to 0.979851728251195. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-over-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 96 (feat 'CNV', ichorcna_strat '(0.1, 1]', .metric 'F1'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.96 recall fraction · higher Uncertainty: 95% CI 0.9516206028711 to 0.967581665568344. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-over-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 94 (feat 'CNV', ichorcna_strat '(0.1, 1]', .metric 'Sensitivity'), column D 'mean'
Configuration: UNITE XGBoost, Copy number aberration (CNA) (Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.996 specificity fraction · higher Uncertainty: 95% CI 0.992722495440018 to 0.998460370396512. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source UNITE XGBoost Copy number aberration (CNA), ichorCNA tumour fraction above 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-over-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_xgb_x1-x6, row 95 (feat 'CNV', ichorcna_strat '(0.1, 1]', .metric 'Specificity'), column D 'mean'

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A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing · Original source · Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML
Wang et al. 2026, Data file S2 (model scores and summary statistics) · Original source · ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1

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Stable ID: ctdnafrag-20261009-config-wang2026-unite-xgb-cna

areas: dna-genomes
contexts: clinical_research
method types: supervised_machine_learning
reported name: CNV
foundation model eligible: false
source locator: Data file S2 sheet STATS_xgb_x1-x6 column 'feat'; Methods P49
missing metadata: version: reason: unreported; note: No UNITE release or code commit is printed for these cross-validation models
parameters: Features: Copy number aberration (CNA). XGBoost with nested cross-validation (StratifiedGroupKFold, randomized grid search); keras v3.3.3 and scikit-learn v1.4.2.

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