Configuration

CNN-RNN (DeepProtein benchmark)

Configuration of CNN-RNN: sequence model; default DeepProtein configuration.

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

34 evaluations · 34 results. Different protocols are not a single leaderboard.

Sorted by Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale)	MAE (mean absolute error)	AUPRC (area under the precision-recall curve, printed on a 0 to 100 scale)	R² (coefficient of determination)	Accuracy (accuracy, printed on a 0 to 100 scale)	AUROC (area under the ROC curve, printed on a 0 to 100 scale)	Spearman (Spearman rank correlation, as defined in the article's Evaluation metrics section; supervised split)
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Secondary structure prediction (TAPE): Averaged F1 ↑, Secondary structure prediction (TAPE)	98.80 ± 0.00% (best in column)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Binary localization (PEER): Averaged F1 ↑, Binary localization (PEER)	88.56 ± 1.03%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Human PPI (PEER): Averaged F1 ↑, Human PPI (PEER)	69.87 ± 2.59%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein SAbDab-Liberis paratope prediction: Averaged F1 ↑, SAbDab-Liberis paratope prediction	69.34 ± 0.10%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Solubility (PEER): Averaged F1 ↑, Solubility (PEER)	60.87 ± 3.19%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Yeast PPI (PEER): Averaged F1 ↑, Yeast PPI (PEER)	47.20 ± 3.62%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein PDB-Jespersen epitope prediction: Averaged F1 ↑, PDB-Jespersen epitope prediction	44.82 ± 1.69%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Subcellular localization (PEER): Averaged F1 ↑, Subcellular localization (PEER)	38.21 ± 0.57%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein IEDB epitope prediction: Averaged F1 ↑, IEDB epitope prediction	10.96 ± 0.26%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Fold classification (TAPE): Averaged F1 ↑, Fold classification (TAPE)	1.06 ± 0.01%	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein beta-lactamase (PEER): MAE ↓, beta-lactamase (PEER)	Not reported	0.157 ± 0.002	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein beta-lactamase (PEER): ρ ↑, beta-lactamase (PEER)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	0.695±0.012 (best in column)
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Binary localization (PEER): PR-AUC ↑, Binary localization (PEER)	Not reported	Not reported	91.43 ± 0.45% (best in column)	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein CRISPR-Leenay repair outcome: MAE ↓, CRISPR-Leenay repair outcome	Not reported	0.0755±0.0010 (best in column)	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein CRISPR-Leenay repair outcome: R2 ↑, CRISPR-Leenay repair outcome	Not reported	Not reported	Not reported	0.771 ± 0.004	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Fluorescence (TAPE): MAE ↓, Fluorescence (TAPE)	Not reported	0.268 ± 0.002	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Fluorescence (TAPE): ρ ↑, Fluorescence (TAPE)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	0.678±0.001
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Fold classification (TAPE): Acc ↑, Fold classification (TAPE)	Not reported	Not reported	Not reported	Not reported	7.50 ± 0.01%	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Human PPI (PEER): PR-AUC ↑, Human PPI (PEER)	Not reported	Not reported	70.45±2.68%	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein IEDB epitope prediction: ROC-AUC ↑, IEDB epitope prediction	Not reported	Not reported	Not reported	Not reported	Not reported	55.47±0.23%	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein PDB-Jespersen epitope prediction: ROC-AUC ↑, PDB-Jespersen epitope prediction	Not reported	Not reported	Not reported	Not reported	Not reported	70.10±0.97%	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein PPI affinity (PEER): MAE ↓, PPI affinity (PEER)	Not reported	1.886 ± 0.108	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein PPI affinity (PEER): R2 ↑, PPI affinity (PEER)	Not reported	Not reported	Not reported	0.584 ± 0.026	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein SAbDab-Chen antibody developability: MAE ↓, SAbDab-Chen antibody developability	Not reported	0.226±0.001	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein SAbDab-Chen antibody developability: R2 ↑, SAbDab-Chen antibody developability	Not reported	Not reported	Not reported	0.486 ± 0.012	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein SAbDab-Liberis paratope prediction: ROC-AUC ↑, SAbDab-Liberis paratope prediction	Not reported	Not reported	Not reported	Not reported	Not reported	96.75 ± 0.10**% (best in column)	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Secondary structure prediction (TAPE): Acc ↑, Secondary structure prediction (TAPE)	Not reported	Not reported	Not reported	Not reported	99.93 ± 0.00% (best in column)	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Solubility (PEER): PR-AUC ↑, Solubility (PEER)	Not reported	Not reported	75.46±0.03%	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Stability (TAPE): MAE ↓, Stability (TAPE)	Not reported	0.323 ± 0.001	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Stability (TAPE): ρ ↑, Stability (TAPE)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	0.635±0.025
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Subcellular localization (PEER): Acc ↑, Subcellular localization (PEER)	Not reported	Not reported	Not reported	Not reported	52.58 ± 0.11%	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein TAP antibody developability: MAE ↓, TAP antibody developability	Not reported	0.712 ± 0.069**	Not reported	Not reported	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein TAP antibody developability: R2 ↑, TAP antibody developability	Not reported	Not reported	Not reported	0.972 ± 0.008** (best in column)	Not reported	Not reported	Not reported
CNN-RNN (DeepProtein benchmark)Evaluation	DeepProtein Yeast PPI (PEER): PR-AUC ↑, Yeast PPI (PEER)	Not reported	Not reported	53.28±0.85%	Not reported	Not reported	Not reported	Not reported

All 34 result rows with coverage, uncertainty and sources

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein beta-lactamase (PEER): MAE ↓ Dataset: beta-lactamase (PEER)	0.157 ± 0.002 MAE (mean absolute error) unit-unreported · lower Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein beta-lactamase (PEER) (MAE ↓) lit-protein-20261011-deepprotein-protocol-beta-lactamase-mae Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 2. row 'CNN-RNN', column group 'β-lactamase', column 'MAE ↓'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein beta-lactamase (PEER): ρ ↑ Dataset: beta-lactamase (PEER)	0.695±0.012 Spearman (Spearman rank correlation, as defined in the article's Evaluation metrics section; supervised split) unitless · higher Uncertainty: SD 0.012 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein beta-lactamase (PEER) (ρ ↑) lit-protein-20261011-deepprotein-protocol-beta-lactamase-rho Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 2. row 'CNN-RNN', column group 'β-lactamase', column 'ρ ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Binary localization (PEER): Averaged F1 ↑ Dataset: Binary localization (PEER)	88.56 ± 1.03% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 1.03 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Binary localization (PEER) (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-binary-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 3. row 'CNN-RNN', column group 'Binary', column 'Averaged F1 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Binary localization (PEER): PR-AUC ↑ Dataset: Binary localization (PEER)	91.43 ± 0.45% AUPRC (area under the precision-recall curve, printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.45 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Binary localization (PEER) (PR-AUC ↑) lit-protein-20261011-deepprotein-protocol-binary-pr-auc Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 3. row 'CNN-RNN', column group 'Binary', column 'PR-AUC ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein CRISPR-Leenay repair outcome: MAE ↓ Dataset: CRISPR-Leenay repair outcome	0.0755±0.0010 MAE (mean absolute error) unit-unreported · lower Uncertainty: SD 0.0010 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein CRISPR-Leenay repair outcome (MAE ↓) lit-protein-20261011-deepprotein-protocol-crispr-leenay-mae Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 6. row 'CNN-RNN', column group 'CRISPR-Leenay', column 'MAE ↓'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein CRISPR-Leenay repair outcome: R2 ↑ Dataset: CRISPR-Leenay repair outcome	0.771 ± 0.004 R² (coefficient of determination) unitless · higher Uncertainty: SD 0.004 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein CRISPR-Leenay repair outcome (R2 ↑) lit-protein-20261011-deepprotein-protocol-crispr-leenay-r2 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 6. row 'CNN-RNN', column group 'CRISPR-Leenay', column 'R2 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Fluorescence (TAPE): MAE ↓ Dataset: Fluorescence (TAPE)	0.268 ± 0.002 MAE (mean absolute error) unit-unreported · lower Uncertainty: SD 0.002 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Fluorescence (TAPE) (MAE ↓) lit-protein-20261011-deepprotein-protocol-fluorescence-mae Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 2. row 'CNN-RNN', column group 'Fluorescence', column 'MAE ↓'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Fluorescence (TAPE): ρ ↑ Dataset: Fluorescence (TAPE)	0.678±0.001 Spearman (Spearman rank correlation, as defined in the article's Evaluation metrics section; supervised split) unitless · higher Uncertainty: SD 0.001 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Fluorescence (TAPE) (ρ ↑) lit-protein-20261011-deepprotein-protocol-fluorescence-rho Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 2. row 'CNN-RNN', column group 'Fluorescence', column 'ρ ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Fold classification (TAPE): Acc ↑ Dataset: Fold classification (TAPE)	7.50 ± 0.01% Accuracy (accuracy, printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.01 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Fold classification (TAPE) (Acc ↑) lit-protein-20261011-deepprotein-protocol-fold-acc Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 7. row 'CNN-RNN', column group 'Fold', column 'Acc ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Fold classification (TAPE): Averaged F1 ↑ Dataset: Fold classification (TAPE)	1.06 ± 0.01% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.01 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Fold classification (TAPE) (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-fold-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 7. row 'CNN-RNN', column group 'Fold', column 'Averaged F1 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Human PPI (PEER): Averaged F1 ↑ Dataset: Human PPI (PEER)	69.87 ± 2.59% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 2.59 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Human PPI (PEER) (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-human-ppi-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 4. row 'CNN-RNN', column group 'Human PPI', column 'Averaged F1 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Human PPI (PEER): PR-AUC ↑ Dataset: Human PPI (PEER)	70.45±2.68% AUPRC (area under the precision-recall curve, printed on a 0 to 100 scale) percent · higher Uncertainty: SD 2.68 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Human PPI (PEER) (PR-AUC ↑) lit-protein-20261011-deepprotein-protocol-human-ppi-pr-auc Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 4. row 'CNN-RNN', column group 'Human PPI', column 'PR-AUC ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein IEDB epitope prediction: Averaged F1 ↑ Dataset: IEDB epitope prediction	10.96 ± 0.26% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.26 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein IEDB epitope prediction (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-iedb-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 5. row 'CNN-RNN', column group 'IEDB', column 'Averaged F1 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein IEDB epitope prediction: ROC-AUC ↑ Dataset: IEDB epitope prediction	55.47±0.23% AUROC (area under the ROC curve, printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.23 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein IEDB epitope prediction (ROC-AUC ↑) lit-protein-20261011-deepprotein-protocol-iedb-roc-auc Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 5. row 'CNN-RNN', column group 'IEDB', column 'ROC-AUC ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein PDB-Jespersen epitope prediction: Averaged F1 ↑ Dataset: PDB-Jespersen epitope prediction	44.82 ± 1.69% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 1.69 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein PDB-Jespersen epitope prediction (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-pdb-jespersen-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 5. row 'CNN-RNN', column group 'PDB-Jespersen', column 'Averaged F1 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein PDB-Jespersen epitope prediction: ROC-AUC ↑ Dataset: PDB-Jespersen epitope prediction	70.10±0.97% AUROC (area under the ROC curve, printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.97 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein PDB-Jespersen epitope prediction (ROC-AUC ↑) lit-protein-20261011-deepprotein-protocol-pdb-jespersen-roc-auc Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 5. row 'CNN-RNN', column group 'PDB-Jespersen', column 'ROC-AUC ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein PPI affinity (PEER): MAE ↓ Dataset: PPI affinity (PEER)	1.886 ± 0.108 MAE (mean absolute error) unit-unreported · lower Uncertainty: SD 0.108 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein PPI affinity (PEER) (MAE ↓) lit-protein-20261011-deepprotein-protocol-ppi-affinity-mae Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 4. row 'CNN-RNN', column group 'PPI Affinity', column 'MAE ↓'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein PPI affinity (PEER): R2 ↑ Dataset: PPI affinity (PEER)	0.584 ± 0.026 R² (coefficient of determination) unitless · higher Uncertainty: SD 0.026 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein PPI affinity (PEER) (R2 ↑) lit-protein-20261011-deepprotein-protocol-ppi-affinity-r2 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 4. row 'CNN-RNN', column group 'PPI Affinity', column 'R2 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein SAbDab-Chen antibody developability: MAE ↓ Dataset: SAbDab-Chen antibody developability	0.226±0.001 MAE (mean absolute error) unit-unreported · lower Uncertainty: SD 0.001 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein SAbDab-Chen antibody developability (MAE ↓) lit-protein-20261011-deepprotein-protocol-sabdab-chen-mae Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 6. row 'CNN-RNN', column group 'SAbDab-Chen', column 'MAE ↓'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein SAbDab-Chen antibody developability: R2 ↑ Dataset: SAbDab-Chen antibody developability	0.486 ± 0.012 R² (coefficient of determination) unitless · higher Uncertainty: SD 0.012 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein SAbDab-Chen antibody developability (R2 ↑) lit-protein-20261011-deepprotein-protocol-sabdab-chen-r2 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 6. row 'CNN-RNN', column group 'SAbDab-Chen', column 'R2 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein SAbDab-Liberis paratope prediction: Averaged F1 ↑ Dataset: SAbDab-Liberis paratope prediction	69.34 ± 0.10% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.10 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein SAbDab-Liberis paratope prediction (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-sabdab-liberis-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 5. row 'CNN-RNN', column group 'SAbDab-Liberis', column 'Averaged F1 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein SAbDab-Liberis paratope prediction: ROC-AUC ↑ Dataset: SAbDab-Liberis paratope prediction	96.75 ± 0.10%** AUROC (area under the ROC curve, printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.10 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein SAbDab-Liberis paratope prediction (ROC-AUC ↑) lit-protein-20261011-deepprotein-protocol-sabdab-liberis-roc-auc Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 5. row 'CNN-RNN', column group 'SAbDab-Liberis', column 'ROC-AUC ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Secondary structure prediction (TAPE): Acc ↑ Dataset: Secondary structure prediction (TAPE)	99.93 ± 0.00% Accuracy (accuracy, printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.00 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Secondary structure prediction (TAPE) (Acc ↑) lit-protein-20261011-deepprotein-protocol-secondary-structure-acc Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 7. row 'CNN-RNN', column group 'Secondary Structure', column 'Acc ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Secondary structure prediction (TAPE): Averaged F1 ↑ Dataset: Secondary structure prediction (TAPE)	98.80 ± 0.00% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 0.00 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Secondary structure prediction (TAPE) (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-secondary-structure-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 7. row 'CNN-RNN', column group 'Secondary Structure', column 'Averaged F1 ↑'
Configuration: CNN-RNN (DeepProtein benchmark)	Protocol: DeepProtein Solubility (PEER): Averaged F1 ↑ Dataset: Solubility (PEER)	60.87 ± 3.19% Macro F1 (averaged macro F1 (Figure 3 caption: 'averaged macro F1'), printed on a 0 to 100 scale) percent · higher Uncertainty: SD 3.19 Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons Figure 3's caption calls the regression metric 'Spearman (Pearson) Coefficient' while the Evaluation metrics section defines rho as Spearman rank correlation and R2 separately. The section's definition is taken as authoritative and the rho columns are recorded as Spearman; the caption wording is noted so a reader of the figure is not surprised.DeepProtein: deep learning library and benchmark for protein sequence learning · Tables 2, 4 and 6 column headings 'rho' and 'R2'; Figure 3 caption Methods, coverage and source CNN-RNN on DeepProtein Solubility (PEER) (Averaged F1 ↑) lit-protein-20261011-deepprotein-protocol-solubility-avg-f1 Aggregation: Not reported DeepProtein: deep learning library and benchmark for protein sequence learning · Table 2. row 'CNN-RNN', column group 'Solubility', column 'Averaged F1 ↑'

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DeepProtein: deep learning library and benchmark for protein sequence learning · Original source · Version of record, Bioinformatics 2025 (PMC12553333 JATS full text)

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Stable ID: lit-protein-20261011-deepprotein-config-cnn-rnn

areas: protein-function
contexts: research
method types: specialist
reported name: CNN-RNN
reported configuration: sequence model; default DeepProtein configuration (Table 9)
foundation model eligible: false
source locator: Tables 2 to 8, row 'CNN-RNN'; Table 9 for the default configuration
missing metadata: version: reason: unreported; note: The tables name the model without a release version
provenance note: CNN-RNN as trained and evaluated in the DeepProtein benchmark (sequence model).

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