Configuration

DNABERT (3-mer)

Genome language model fine-tuned on each GUE dataset by the DNABERT-2 authors.

Overview

Genome language model fine-tuned on each GUE dataset by the DNABERT-2 authors.

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

Evaluations and results

28 evaluations · 28 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: DNABERT (3-mer)	Task: GUE CORE-PROMOTER-DETECTION-ALL: Core promoter detection, dataset all Dataset subset: GUE Core promoter detection, all (GUE split)	70.9% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE CORE-PROMOTER-DETECTION-ALL: Core promoter detection, dataset all Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Core promoter detection all)
Configuration: DNABERT (3-mer)	Task: GUE CORE-PROMOTER-DETECTION-NOTATA: Core promoter detection, dataset notata Dataset subset: GUE Core promoter detection, notata (GUE split)	69.8% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE CORE-PROMOTER-DETECTION-NOTATA: Core promoter detection, dataset notata Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Core promoter detection notata)
Configuration: DNABERT (3-mer)	Task: GUE CORE-PROMOTER-DETECTION-TATA: Core promoter detection, dataset tata Dataset subset: GUE Core promoter detection, tata (GUE split)	78.2% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE CORE-PROMOTER-DETECTION-TATA: Core promoter detection, dataset tata Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Core promoter detection tata)
Configuration: DNABERT (3-mer)	Task: GUE COVID-VARIANT-CLASSIFICATION-COVID: Covid variant classification, dataset Covid Dataset subset: GUE Covid variant classification, Covid (GUE split)	62.2% f1 percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE COVID-VARIANT-CLASSIFICATION-COVID: Covid variant classification, dataset Covid Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Covid variant classification Covid)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3: Epigenetic marks prediction, dataset H3 Dataset subset: GUE Epigenetic marks prediction, H3 (GUE split)	74.2% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3: Epigenetic marks prediction, dataset H3 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K14AC: Epigenetic marks prediction, dataset H3K14ac Dataset subset: GUE Epigenetic marks prediction, H3K14ac (GUE split)	42.1% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3K14AC: Epigenetic marks prediction, dataset H3K14ac Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3K14ac)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K36ME3: Epigenetic marks prediction, dataset H3K36me3 Dataset subset: GUE Epigenetic marks prediction, H3K36me3 (GUE split)	48.5% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3K36ME3: Epigenetic marks prediction, dataset H3K36me3 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3K36me3)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME1: Epigenetic marks prediction, dataset H3K4me1 Dataset subset: GUE Epigenetic marks prediction, H3K4me1 (GUE split)	43% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME1: Epigenetic marks prediction, dataset H3K4me1 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3K4me1)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME2: Epigenetic marks prediction, dataset H3K4me2 Dataset subset: GUE Epigenetic marks prediction, H3K4me2 (GUE split)	31.3% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME2: Epigenetic marks prediction, dataset H3K4me2 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3K4me2)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME3: Epigenetic marks prediction, dataset H3K4me3 Dataset subset: GUE Epigenetic marks prediction, H3K4me3 (GUE split)	28.9% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME3: Epigenetic marks prediction, dataset H3K4me3 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3K4me3)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K79ME3: Epigenetic marks prediction, dataset H3K79me3 Dataset subset: GUE Epigenetic marks prediction, H3K79me3 (GUE split)	60.1% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3K79ME3: Epigenetic marks prediction, dataset H3K79me3 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3K79me3)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K9AC: Epigenetic marks prediction, dataset H3K9ac Dataset subset: GUE Epigenetic marks prediction, H3K9ac (GUE split)	50.5% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H3K9AC: Epigenetic marks prediction, dataset H3K9ac Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H3K9ac)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H4: Epigenetic marks prediction, dataset H4 Dataset subset: GUE Epigenetic marks prediction, H4 (GUE split)	78.3% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H4: Epigenetic marks prediction, dataset H4 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H4)
Configuration: DNABERT (3-mer)	Task: GUE EPIGENETIC-MARKS-PREDICTION-H4AC: Epigenetic marks prediction, dataset H4ac Dataset subset: GUE Epigenetic marks prediction, H4ac (GUE split)	38.6% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE EPIGENETIC-MARKS-PREDICTION-H4AC: Epigenetic marks prediction, dataset H4ac Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Epigenetic marks prediction H4ac)
Configuration: DNABERT (3-mer)	Task: GUE PROMOTER-DETECTION-ALL: Promoter detection, dataset all Dataset subset: GUE Promoter detection, all (GUE split)	90.4% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE PROMOTER-DETECTION-ALL: Promoter detection, dataset all Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Promoter detection all)
Configuration: DNABERT (3-mer)	Task: GUE PROMOTER-DETECTION-NOTATA: Promoter detection, dataset notata Dataset subset: GUE Promoter detection, notata (GUE split)	93.6% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE PROMOTER-DETECTION-NOTATA: Promoter detection, dataset notata Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Promoter detection notata)
Configuration: DNABERT (3-mer)	Task: GUE PROMOTER-DETECTION-TATA: Promoter detection, dataset tata Dataset subset: GUE Promoter detection, tata (GUE split)	69.8% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE PROMOTER-DETECTION-TATA: Promoter detection, dataset tata Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Promoter detection tata)
Configuration: DNABERT (3-mer)	Task: GUE SPLICE-SITE-PREDICTION-RECONSTRUCT: Splice site prediction, dataset Reconstruct Dataset subset: GUE Splice site prediction, Reconstruct (GUE split)	84.1% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE SPLICE-SITE-PREDICTION-RECONSTRUCT: Splice site prediction, dataset Reconstruct Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Splice site prediction Reconstruct)
Configuration: DNABERT (3-mer)	Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-0: Transcription factor prediction (human), dataset 0 Dataset subset: GUE Transcription factor prediction (human), 0 (GUE split)	68% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-0: Transcription factor prediction (human), dataset 0 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Transcription factor prediction (human) 0)
Configuration: DNABERT (3-mer)	Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-1: Transcription factor prediction (human), dataset 1 Dataset subset: GUE Transcription factor prediction (human), 1 (GUE split)	70.9% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-1: Transcription factor prediction (human), dataset 1 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Transcription factor prediction (human) 1)
Configuration: DNABERT (3-mer)	Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-2: Transcription factor prediction (human), dataset 2 Dataset subset: GUE Transcription factor prediction (human), 2 (GUE split)	60.5% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-2: Transcription factor prediction (human), dataset 2 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Transcription factor prediction (human) 2)
Configuration: DNABERT (3-mer)	Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-3: Transcription factor prediction (human), dataset 3 Dataset subset: GUE Transcription factor prediction (human), 3 (GUE split)	53% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-3: Transcription factor prediction (human), dataset 3 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Transcription factor prediction (human) 3)
Configuration: DNABERT (3-mer)	Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-4: Transcription factor prediction (human), dataset 4 Dataset subset: GUE Transcription factor prediction (human), 4 (GUE split)	69.8% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-4: Transcription factor prediction (human), dataset 4 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Transcription factor prediction (human) 4)
Configuration: DNABERT (3-mer)	Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-0: Transcription factor prediction (mouse), dataset 0 Dataset subset: GUE Transcription factor prediction (mouse), 0 (GUE split)	42.3% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-0: Transcription factor prediction (mouse), dataset 0 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Transcription factor prediction (mouse) 0)
Configuration: DNABERT (3-mer)	Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-1: Transcription factor prediction (mouse), dataset 1 Dataset subset: GUE Transcription factor prediction (mouse), 1 (GUE split)	79.1% mcc percent · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source DNABERT (3-mer) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-1: Transcription factor prediction (mouse), dataset 1 Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Aggregation: Not reported DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT (3-mer)), column(Transcription factor prediction (mouse) 1)

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