| Configuration: GlycanAA | Protocol: GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.92420000000000002 ± 0.015649920127591699 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.015649920127591699 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGlycanAA on GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A28:E28 (mean D28, SD E28) |
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| Configuration: GlycanAA | Protocol: GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.59389999999999998 ± 0.095006157695172497 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.095006157695172497 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGlycanAA on GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A29:E29 (mean D29, SD E29) |
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| Pipeline: GlycanGT | Protocol: GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.90243017772941603 ± 0.017489392022112599 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.017489392022112599 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGlycanGT on GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A67:E67 (mean D67, SD E67) |
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| Pipeline: GlycanGT | Protocol: GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.66958587101112299 ± 0.082979941298309295 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.082979941298309295 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGlycanGT on GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A68:E68 (mean D68, SD E68) |
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| Configuration: Graphormer | Protocol: GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.89626399999999995 ± 0.019352999999999999 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.019352999999999999 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGraphormer on GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A98:E98 (mean D98, SD E98) |
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| Configuration: Graphormer | Protocol: GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.47853000000000001 ± 0.053871000000000002 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.053871000000000002 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceGraphormer on GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A99:E99 (mean D99, SD E99) |
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| Configuration: RGCN | Protocol: GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.121146173376858 ± 0.0041196288326443 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.0041196288326443 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRGCN on GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A5:E5 (mean D5, SD E5) |
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| Configuration: RGCN | Protocol: GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.12720418842586201 ± 0.0063563233302618002 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.0063563233302618002 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRGCN on GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A6:E6 (mean D6, SD E6) |
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| Configuration: SweetNet | Protocol: GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.89662676822633303 ± 0.013148036968002701 accuracy fraction · higher Uncertainty: type: standard_deviation; value: 0.013148036968002701 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceSweetNet on GlycanML kingdom Accuracy: GlycanGT study: kingdom Accuracy Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A45:E45 (mean D45, SD E45) |
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| Configuration: SweetNet | Protocol: GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Dataset subset: SugarBase taxonomy kingdom; GlycanML official motif split (GlycanML split) | 0.54447204516927605 ± 0.016810148206062699 macro_f1 dimensionless · higher Uncertainty: type: standard_deviation; value: 0.016810148206062699 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceSweetNet on GlycanML kingdom Macro-F1: GlycanGT study: kingdom Macro-F1 Taxonomy: 13,209 glycans total across eight levels, 4–1,737 classes per level. 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!A55:E55 (mean D55, SD E55) |
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