Published comparison; transcribed, not reproduced.
These checks assess whether the evidence supports a reproducible investigation. A source-checked score alone does not meet these requirements.
Release 2026-10-09-ba02f2f4a36e · Evidence verified: Not verified
Exact outcomes, predictions, identifiers and evaluator are connected.
Missing or unresolved evidence
Verified: Not verified
Replay evidence includes annotations and an assessment of dependence. Unknown independence permits descriptive analysis only.
Missing or unresolved evidence
Verified: Not verified
A pinned recipe describes the inputs, environment and resource requirements.
Missing or unresolved evidence
Verified: Not verified
Separate data and exposure records support an independent test.
Missing or unresolved evidence
Verified: Not verified
Readiness describes the evidence in this release. Availability on your computer is checked separately when an investigation runs. Existing data exposure can prevent independent validation even when files are available.
No verified artifact manifest is connected to this record yet. The gaps above identify what is needed before analysis can begin.
1 evaluation · 16 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.536 accuracy fraction · higher Uncertainty: 95% CI 0.531049658385818 to 0.541013108198761. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 50 (feat 'TF', ichorcna_strat 'all', .metric 'test_acc'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.647 accuracy fraction · higher Uncertainty: 95% CI 0.642731552938907 to 0.650504861551433. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 51 (feat 'TF', ichorcna_strat 'all', .metric 'test_acc_95spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.653 accuracy fraction · higher Uncertainty: 95% CI 0.643977997471832 to 0.659762092472169. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 52 (feat 'TF', ichorcna_strat 'all', .metric 'test_acc_98spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.634 accuracy fraction · higher Uncertainty: 95% CI 0.616718967325738 to 0.650376938481781. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 53 (feat 'TF', ichorcna_strat 'all', .metric 'test_acc_99spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.866 auprc unitless · higher Uncertainty: 95% CI 0.862608963922692 to 0.869544218585427. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 54 (feat 'TF', ichorcna_strat 'all', .metric 'test_auprc'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.642 auroc unitless · higher Uncertainty: 95% CI 0.634830457106185 to 0.649129313044353. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 55 (feat 'TF', ichorcna_strat 'all', .metric 'test_auroc'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.552 f1-score fraction · higher Uncertainty: 95% CI 0.544499419390956 to 0.559421831257033. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 56 (feat 'TF', ichorcna_strat 'all', .metric 'test_f1'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.539 f1-score fraction · higher Uncertainty: 95% CI 0.529037073340783 to 0.548138880590275. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 57 (feat 'TF', ichorcna_strat 'all', .metric 'test_f1_95spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.49 f1-score fraction · higher Uncertainty: 95% CI 0.461755353357025 to 0.507804537144719. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 58 (feat 'TF', ichorcna_strat 'all', .metric 'test_f1_98spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.418 f1-score fraction · higher Uncertainty: 95% CI 0.368437521835668 to 0.464128940675072. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 59 (feat 'TF', ichorcna_strat 'all', .metric 'test_f1_99spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.956 precision fraction · higher Uncertainty: 95% CI 0.950276128618534 to 0.961981949281337. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 60 (feat 'TF', ichorcna_strat 'all', .metric 'test_ppv'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.374 sensitivity-at-95-percent-specificity fraction · higher Uncertainty: 95% CI 0.365694916004718 to 0.382975215973555. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 61 (feat 'TF', ichorcna_strat 'all', .metric 'test_sen_95spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.328 sensitivity-at-98-percent-specificity fraction · higher Uncertainty: 95% CI 0.310630289471739 to 0.341297092803188. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 62 (feat 'TF', ichorcna_strat 'all', .metric 'test_sen_98spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.279 sensitivity-at-99-percent-specificity fraction · higher Uncertainty: 95% CI 0.241956979379117 to 0.309680705599319. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 63 (feat 'TF', ichorcna_strat 'all', .metric 'test_sen_99spe'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.389 recall fraction · higher Uncertainty: 95% CI 0.381124106659517 to 0.39592438717016. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 64 (feat 'TF', ichorcna_strat 'all', .metric 'test_sensitivity'), column D 'mean' |
| Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, 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 | 0.949 specificity fraction · higher Uncertainty: 95% CI 0.942030519151722 to 0.956299702195182. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceichorCNA-TF logistic regression, 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_lr, row 65 (feat 'TF', ichorcna_strat 'all', .metric 'test_specificity'), column D 'mean' |
Source checking is not independent reproduction. Release 2026-10-09-ba02f2f4a36e.
ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-all
Metadata review: source checked. Unreported conditions prevent automatic comparisons.
No execution recipe has been verified for this exact configuration and evaluation. A benchmark's general instructions may use different inputs, splits or model settings.
Reproducing this published result requires matching its model configuration, data, split and scorer. Source checking or a successful smoke test does not establish score reproduction.
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
38 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.comparison.adaptation Model trained per outer fold with inner hyperparameter search Context-only references | A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.comparison.adaptation Model trained per outer fold with inner hyperparameter search Context-only references | Wang et al. 2026, Data file S2 (model scores and summary statistics) Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx |
| attributes.comparison.aggregation Mean, median, SD, 95% CI and SEM over 50 outer test folds Context-only references | A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.comparison.aggregation Mean, median, SD, 95% CI and SEM over 50 outer test folds Context-only references | Wang et al. 2026, Data file S2 (model scores and summary statistics) Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx |
| attributes.comparison.budget Not reported Context-only references | A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.comparison.budget Not reported Context-only references | Wang et al. 2026, Data file S2 (model scores and summary statistics) Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 | missing or unspecified No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx |
| attributes.comparison.dataset_version Wang et al. 2026 cross-validation set Context-only references | A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.comparison.dataset_version Wang et al. 2026 cross-validation set Context-only references | Wang et al. 2026, Data file S2 (model scores and summary statistics) Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx |
| attributes.comparison.inputs ichorCNA tumour fraction from 0.1x sWGS Context-only references | A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.comparison.inputs ichorCNA tumour fraction from 0.1x sWGS Context-only references | Wang et al. 2026, Data file S2 (model scores and summary statistics) Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat 'all' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 | not individually reviewed No individual claim review recorded independent paper Audit detailsField: Source artifact SHA-256: Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx |
Release 2026-10-09-ba02f2f4a36e · Record review: source checked
Stable ID: ctdnafrag-20261009-eval-wang2026-ichorcna-tf-tf-all