Dataset subset

SugarBase immunogenicity; GlycanML official motif split (GlycanML split)

Dataset subset reported in GlycanGT published supplementary archive, Table S4. Exact split manifest remains unextracted; source-table identity is retained.

Evaluation results

13 evaluations · 13 metric rows. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GlycanAA	Protocol: GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.86209999999999998 ± 0 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source GlycanAA on GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A42:E42 (mean D42, SD E42)
Configuration: GlycanAA	Protocol: GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.70573333333333299 ± 0.069754736995657304 auprc dimensionless · higher Uncertainty: type: standard_deviation; value: 0.069754736995657304 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source GlycanAA on GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A43:E43 (mean D43, SD E43)
Pipeline: GlycanGT	Protocol: GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.93333333333333302 ± 0.031853807955289602 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.031853807955289602 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source GlycanGT on GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A81:E81 (mean D81, SD E81)
Pipeline: GlycanGT	Protocol: GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.84419033765294005 ± 0.0036259797661145998 auprc dimensionless · higher Uncertainty: type: standard_deviation; value: 0.0036259797661145998 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source GlycanGT on GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A83:E83 (mean D83, SD E83)
Pipeline: GlycanGT	Protocol: GlycanML immunogenicity Macro-F1: GlycanGT study: immunogenicity Macro-F1 Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.87036334625073897 ± 0.065078625040751806 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.065078625040751806 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source GlycanGT on GlycanML immunogenicity Macro-F1: GlycanGT study: immunogenicity Macro-F1 Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A82:E82 (mean D82, SD E82)
Configuration: Graphormer	Protocol: GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.90344800000000003 ± 0.023890000000000002 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.023890000000000002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source Graphormer on GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A96:E96 (mean D96, SD E96)
Configuration: Graphormer	Protocol: GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.71593499999999999 ± 0.0047320000000000001 auprc dimensionless · higher Uncertainty: type: standard_deviation; value: 0.0047320000000000001 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source Graphormer on GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A97:E97 (mean D97, SD E97)
Configuration: RGCN	Protocol: GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.073563218390804597 ± 0.0039817259944112003 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.0039817259944112003 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source RGCN on GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A19:E19 (mean D19, SD E19)
Configuration: RGCN	Protocol: GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.69553233559432803 ± 0.0031631481710155001 auprc dimensionless · higher Uncertainty: type: standard_deviation; value: 0.0031631481710155001 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source RGCN on GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A21:E21 (mean D21, SD E21)
Configuration: RGCN	Protocol: GlycanML immunogenicity Macro-F1: GlycanGT study: immunogenicity Macro-F1 Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.073416647085454895 ± 0.0038588027543180999 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.0038588027543180999 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source RGCN on GlycanML immunogenicity Macro-F1: GlycanGT study: immunogenicity Macro-F1 Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A20:E20 (mean D20, SD E20)
Configuration: SweetNet	Protocol: GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.91264367816091896 ± 0.0141710666734918 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.0141710666734918 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source SweetNet on GlycanML immunogenicity Accuracy: GlycanGT study: immunogenicity Accuracy Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A52:E52 (mean D52, SD E52)
Configuration: SweetNet	Protocol: GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.76215599999999994 ± 0.043916999999999998 auprc dimensionless · higher Uncertainty: type: standard_deviation; value: 0.043916999999999998 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source SweetNet on GlycanML immunogenicity AUPRC: GlycanGT study: immunogenicity AUPRC Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A64:E64 (mean D64, SD E64)
Configuration: SweetNet	Protocol: GlycanML immunogenicity Macro-F1: GlycanGT study: immunogenicity Macro-F1 Dataset subset: SugarBase immunogenicity; GlycanML official motif split (GlycanML split)	0.79519622360774 ± 0.0284397253036517 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.0284397253036517 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · source checked Methods, coverage and source SweetNet on GlycanML immunogenicity Macro-F1: GlycanGT study: immunogenicity Macro-F1 Immunogenicity: 1,320 glycans total; binary immune activity. Official fixed GlycanML motif-based train/validation/test splits (8:1:1). Section 2.5 reports class-balanced classifiers, train ∪ validation hyperparameter selection by randomized search with 3-fold cross-validation, followed by one evaluation on the held-out test set; complete procedure repeated with three random seeds, reporting mean and standard deviation. GlycanGT large model pretrained with 35% masking provides [Graph] embeddings to SVM/LightGBM; the selected classifier for each S4 row is not identified. Section 2.6 states that all four graph baselines were trained and evaluated on the same datasets/splits; it does not establish that each baseline used the GlycanGT downstream classifier search. SVM search: 10 iterations, RBF/linear, C logU(1e-3,1e2), gamma logU(1e-4,1e-1); LightGBM: 15 randomized iterations. Do not combine with separate original GlycanML-paper protocols. Aggregation: Not reported GlycanGT published supplementary archive, Table S4; glycangt: Journal full-text XML · btag147_supplementary_data.zip / Table_S4.xlsx / Sheet1!A62:E62 (mean D62, SD E62)

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Claims, original sources and review scope · Release 2026-09-23-2b89723c6dd9
Property and statement	Original source and location	Review and provenance
description Dataset subset reported in GlycanGT published supplementary archive, Table S4. Exact split manifest remains unextracted; source-table identity is retained. Context-only references	glycangt: Journal full-text XML Original source ↗ No field-specific location recorded Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Primary article XML snapshot Retrieved: 2026-09-16T20:20:56.439056+00:00	not individually reviewed No individual claim review recorded Audit details Field: `description` Source artifact SHA-256: `53e89a636c868c0329ee7eb6ae92f1028ec891940bc61730a148981b647fbbe5` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
description Dataset subset reported in GlycanGT published supplementary archive, Table S4. Exact split manifest remains unextracted; source-table identity is retained. Context-only references	GlycanGT published supplementary archive, Table S4 Original source ↗ No field-specific location recorded Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Published Bioinformatics btag147 supplementary archive, retrieved 2026-09-23; Table_S4.xlsx SHA-256 d7c35909bac6bcb78ca8fdb32c0463f05e692f15861a8184e65e415e7216f493 Retrieved: 2026-09-23T11:31:33.520570+00:00	not individually reviewed No individual claim review recorded Audit details Field: `description` Source artifact SHA-256: `27748c6c0c0bb07b0105d274fb745fd4dfe702d34b9ee367d1ddacbc71c56ab0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
name SugarBase immunogenicity; GlycanML official motif split (GlycanML split) Context-only references	glycangt: Journal full-text XML Original source ↗ No field-specific location recorded Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Primary article XML snapshot Retrieved: 2026-09-16T20:20:56.439056+00:00	not individually reviewed No individual claim review recorded Audit details Field: `name` Source artifact SHA-256: `53e89a636c868c0329ee7eb6ae92f1028ec891940bc61730a148981b647fbbe5` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
name SugarBase immunogenicity; GlycanML official motif split (GlycanML split) Context-only references	GlycanGT published supplementary archive, Table S4 Original source ↗ No field-specific location recorded Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Published Bioinformatics btag147 supplementary archive, retrieved 2026-09-23; Table_S4.xlsx SHA-256 d7c35909bac6bcb78ca8fdb32c0463f05e692f15861a8184e65e415e7216f493 Retrieved: 2026-09-23T11:31:33.520570+00:00	not individually reviewed No individual claim review recorded Audit details Field: `name` Source artifact SHA-256: `27748c6c0c0bb07b0105d274fb745fd4dfe702d34b9ee367d1ddacbc71c56ab0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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GlycanGT published supplementary archive, Table S4 · Original source · Published Bioinformatics btag147 supplementary archive, retrieved 2026-09-23; Table_S4.xlsx SHA-256 d7c35909bac6bcb78ca8fdb32c0463f05e692f15861a8184e65e415e7216f493
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