Baseline CNN (PyTorch)
The paper's baseline convolutional network, built with PyTorch.
Overview
The paper's baseline convolutional network, built with PyTorch.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
Release 2026-09-17-134cd1815de8 · 18 evaluations · 18 metric rows. Different protocols are not a single leaderboard. Where several source tables report the same metric, the published comparisons above offer a pooled view that names what it does not hold constant.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-ACCURACY: demo_coding_vs_intergenomic_seqs, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-ACCURACY: demo_coding_vs_intergenomic_seqs, AccuracyDataset subset: demo_coding_vs_intergenomic_seqs (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 87.6% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_coding_vs_intergenomic_seqs), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-F1: demo_coding_vs_intergenomic_seqs, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-F1: demo_coding_vs_intergenomic_seqs, F1 scoreDataset subset: demo_coding_vs_intergenomic_seqs (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 86.8% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_coding_vs_intergenomic_seqs), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-HUMAN-OR-WORM-ACCURACY: demo_human_or_worm, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DEMO-HUMAN-OR-WORM-ACCURACY: demo_human_or_worm, AccuracyDataset subset: demo_human_or_worm (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 93.0% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_human_or_worm), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-HUMAN-OR-WORM-F1: demo_human_or_worm, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DEMO-HUMAN-OR-WORM-F1: demo_human_or_worm, F1 scoreDataset subset: demo_human_or_worm (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 92.8% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_human_or_worm), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-ACCURACY: drosophila_enhancers_stark, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-ACCURACY: drosophila_enhancers_stark, AccuracyDataset subset: drosophila_enhancers_stark (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 58.6% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(drosophila_enhancers_stark), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-F1: drosophila_enhancers_stark, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-F1: drosophila_enhancers_stark, F1 scoreDataset subset: drosophila_enhancers_stark (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 44.5% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(drosophila_enhancers_stark), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-ACCURACY: dummy_mouse_enhancers_ensembl, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-ACCURACY: dummy_mouse_enhancers_ensembl, AccuracyDataset subset: dummy_mouse_enhancers_ensembl (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 69.0% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(dummy_mouse_enhancers_ensembl), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-F1: dummy_mouse_enhancers_ensembl, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-F1: dummy_mouse_enhancers_ensembl, F1 scoreDataset subset: dummy_mouse_enhancers_ensembl (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 70.4% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(dummy_mouse_enhancers_ensembl), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-COHN-ACCURACY: human_enhancers_cohn, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-ENHANCERS-COHN-ACCURACY: human_enhancers_cohn, AccuracyDataset subset: human_enhancers_cohn (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 69.5% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_cohn), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-COHN-F1: human_enhancers_cohn, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-ENHANCERS-COHN-F1: human_enhancers_cohn, F1 scoreDataset subset: human_enhancers_cohn (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 67.1% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_cohn), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-ACCURACY: human_enhancers_ensembl, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-ACCURACY: human_enhancers_ensembl, AccuracyDataset subset: human_enhancers_ensembl (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 68.9% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_ensembl), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-F1: human_enhancers_ensembl, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-F1: human_enhancers_ensembl, F1 scoreDataset subset: human_enhancers_ensembl (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 56.5% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_ensembl), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-ACCURACY: human_ensembl_regulatory, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-ACCURACY: human_ensembl_regulatory, AccuracyDataset subset: human_ensembl_regulatory (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 93.3% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ensembl_regulatory), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-F1: human_ensembl_regulatory, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-F1: human_ensembl_regulatory, F1 scoreDataset subset: human_ensembl_regulatory (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 93.3% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ensembl_regulatory), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-ACCURACY: human_nontata_promoters, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-ACCURACY: human_nontata_promoters, AccuracyDataset subset: human_nontata_promoters (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 84.6% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_nontata_promoters), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-F1: human_nontata_promoters, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-F1: human_nontata_promoters, F1 scoreDataset subset: human_nontata_promoters (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 83.7% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_nontata_promoters), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-OCR-ENSEMBL-ACCURACY: human_ocr_ensembl, Accuracy Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-OCR-ENSEMBL-ACCURACY: human_ocr_ensembl, AccuracyDataset subset: human_ocr_ensembl (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 68.0% accuracy Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ocr_ensembl), column(Baseline CNN (PyTorch) Accuracy) Source checking is not independent reproduction. |
| Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-OCR-ENSEMBL-F1: human_ocr_ensembl, F1 score Configuration: Baseline CNN (PyTorch)Task: Genomic Benchmarks HUMAN-OCR-ENSEMBL-F1: human_ocr_ensembl, F1 scoreDataset subset: human_ocr_ensembl (Genomic Benchmarks split) The paper's own three-layer convolutional baseline, trained on each dataset's training split and scored on its test split. Architecture is in Table 1. Author-reported evaluation · Evaluation metadata: source checked | ||
| 66.1% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedGenomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ocr_ensembl), column(Baseline CNN (PyTorch) F1 score) Source checking is not independent reproduction. |
Evidence table
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Sources and history
Release 2026-09-17-134cd1815de8 · Record review: source checked
1 source records and release history
- Genomic benchmarks: a collection of datasets for genomic sequence classification · Original source · PMC10150520
Technical metadata and extraction receipts
Stable ID: genomic-benchmarks-method-baseline-cnn-pytorch
- areas
- dna-genomes
- source locator
- Table 2, column(Baseline CNN (PyTorch))
- missing metadata
- checkpoint revision: unreported; parameters: unextracted
Related records
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-ACCURACY: demo_coding_vs_intergenomic_seqs, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-F1: demo_coding_vs_intergenomic_seqs, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-HUMAN-OR-WORM-ACCURACY: demo_human_or_worm, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-HUMAN-OR-WORM-F1: demo_human_or_worm, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-ACCURACY: drosophila_enhancers_stark, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-F1: drosophila_enhancers_stark, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-ACCURACY: dummy_mouse_enhancers_ensembl, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-F1: dummy_mouse_enhancers_ensembl, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-COHN-ACCURACY: human_enhancers_cohn, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-COHN-F1: human_enhancers_cohn, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-ACCURACY: human_enhancers_ensembl, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-F1: human_enhancers_ensembl, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-ACCURACY: human_ensembl_regulatory, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-F1: human_ensembl_regulatory, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-ACCURACY: human_nontata_promoters, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-F1: human_nontata_promoters, F1 score
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-OCR-ENSEMBL-ACCURACY: human_ocr_ensembl, Accuracy
- model: Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-OCR-ENSEMBL-F1: human_ocr_ensembl, F1 score