Configuration

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

9 evaluations · 18 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-ACCURACY: demo_coding_vs_intergenomic_seqs, Accuracy Dataset subset: demo_coding_vs_intergenomic_seqs (Genomic Benchmarks split)	87.6% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-ACCURACY: demo_coding_vs_intergenomic_seqs, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_coding_vs_intergenomic_seqs), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-F1: demo_coding_vs_intergenomic_seqs, F1 score Dataset subset: demo_coding_vs_intergenomic_seqs (Genomic Benchmarks split)	86.8% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-CODING-VS-INTERGENOMIC-SEQS-F1: demo_coding_vs_intergenomic_seqs, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_coding_vs_intergenomic_seqs), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DEMO-HUMAN-OR-WORM-ACCURACY: demo_human_or_worm, Accuracy Dataset subset: demo_human_or_worm (Genomic Benchmarks split)	93% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-HUMAN-OR-WORM-ACCURACY: demo_human_or_worm, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_human_or_worm), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DEMO-HUMAN-OR-WORM-F1: demo_human_or_worm, F1 score Dataset subset: demo_human_or_worm (Genomic Benchmarks split)	92.8% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DEMO-HUMAN-OR-WORM-F1: demo_human_or_worm, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(demo_human_or_worm), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-ACCURACY: drosophila_enhancers_stark, Accuracy Dataset subset: drosophila_enhancers_stark (Genomic Benchmarks split)	58.6% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-ACCURACY: drosophila_enhancers_stark, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(drosophila_enhancers_stark), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-F1: drosophila_enhancers_stark, F1 score Dataset subset: drosophila_enhancers_stark (Genomic Benchmarks split)	44.5% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DROSOPHILA-ENHANCERS-STARK-F1: drosophila_enhancers_stark, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(drosophila_enhancers_stark), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-ACCURACY: dummy_mouse_enhancers_ensembl, Accuracy Dataset subset: dummy_mouse_enhancers_ensembl (Genomic Benchmarks split)	69% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-ACCURACY: dummy_mouse_enhancers_ensembl, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(dummy_mouse_enhancers_ensembl), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-F1: dummy_mouse_enhancers_ensembl, F1 score Dataset subset: dummy_mouse_enhancers_ensembl (Genomic Benchmarks split)	70.4% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks DUMMY-MOUSE-ENHANCERS-ENSEMBL-F1: dummy_mouse_enhancers_ensembl, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(dummy_mouse_enhancers_ensembl), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-ENHANCERS-COHN-ACCURACY: human_enhancers_cohn, Accuracy Dataset subset: human_enhancers_cohn (Genomic Benchmarks split)	69.5% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-COHN-ACCURACY: human_enhancers_cohn, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_cohn), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-ENHANCERS-COHN-F1: human_enhancers_cohn, F1 score Dataset subset: human_enhancers_cohn (Genomic Benchmarks split)	67.1% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-COHN-F1: human_enhancers_cohn, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_cohn), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-ACCURACY: human_enhancers_ensembl, Accuracy Dataset subset: human_enhancers_ensembl (Genomic Benchmarks split)	68.9% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-ACCURACY: human_enhancers_ensembl, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_ensembl), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-F1: human_enhancers_ensembl, F1 score Dataset subset: human_enhancers_ensembl (Genomic Benchmarks split)	56.5% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENHANCERS-ENSEMBL-F1: human_enhancers_ensembl, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_enhancers_ensembl), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-ACCURACY: human_ensembl_regulatory, Accuracy Dataset subset: human_ensembl_regulatory (Genomic Benchmarks split)	93.3% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-ACCURACY: human_ensembl_regulatory, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ensembl_regulatory), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-F1: human_ensembl_regulatory, F1 score Dataset subset: human_ensembl_regulatory (Genomic Benchmarks split)	93.3% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-ENSEMBL-REGULATORY-F1: human_ensembl_regulatory, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ensembl_regulatory), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-ACCURACY: human_nontata_promoters, Accuracy Dataset subset: human_nontata_promoters (Genomic Benchmarks split)	84.6% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-ACCURACY: human_nontata_promoters, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_nontata_promoters), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-F1: human_nontata_promoters, F1 score Dataset subset: human_nontata_promoters (Genomic Benchmarks split)	83.7% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-NONTATA-PROMOTERS-F1: human_nontata_promoters, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_nontata_promoters), column(Baseline CNN (PyTorch) F1 score)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-OCR-ENSEMBL-ACCURACY: human_ocr_ensembl, Accuracy Dataset subset: human_ocr_ensembl (Genomic Benchmarks split)	68% accuracy percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-OCR-ENSEMBL-ACCURACY: human_ocr_ensembl, Accuracy 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ocr_ensembl), column(Baseline CNN (PyTorch) Accuracy)
Configuration: Baseline CNN (PyTorch)	Task: Genomic Benchmarks HUMAN-OCR-ENSEMBL-F1: human_ocr_ensembl, F1 score Dataset subset: human_ocr_ensembl (Genomic Benchmarks split)	66.1% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Baseline CNN (PyTorch) on Genomic Benchmarks HUMAN-OCR-ENSEMBL-F1: human_ocr_ensembl, F1 score 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. Aggregation: Not reported Genomic benchmarks: a collection of datasets for genomic sequence classification · Table 2, row(human_ocr_ensembl), column(Baseline CNN (PyTorch) F1 score)

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