Dataset subset reported in GlycanGT published supplementary archive, Table S4. Exact split manifest remains unextracted; source-table identity is retained.
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13 evaluations · 13 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| 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 checkedMethods, coverage and sourceGlycanAA 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 checkedMethods, coverage and sourceGlycanAA 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 checkedMethods, coverage and sourceGlycanGT 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 checkedMethods, coverage and sourceGlycanGT 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 checkedMethods, coverage and sourceGlycanGT 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 checkedMethods, coverage and sourceGraphormer 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 checkedMethods, coverage and sourceGraphormer 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 checkedMethods, coverage and sourceRGCN 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 checkedMethods, coverage and sourceRGCN 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 checkedMethods, coverage and sourceRGCN 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 checkedMethods, coverage and sourceSweetNet 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 checkedMethods, coverage and sourceSweetNet 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 checkedMethods, coverage and sourceSweetNet 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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4 evidence rows matching the loaded filters
| 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 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 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_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 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 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name SugarBase immunogenicity; GlycanML official motif split (GlycanML split) Context-only references | glycangt: Journal full-text XML 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 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| name SugarBase immunogenicity; GlycanML official motif split (GlycanML split) Context-only references | GlycanGT published supplementary archive, Table S4 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 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
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Release 2026-09-29-06401fd5b220 · Record review: source checked
Stable ID: glycangt-2026-table-s4-dataset-sugarbase-immunogenicity-glycanml-official-motif-split