DNABERT-2 (further pre-trained on GUE)
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
Release 2026-09-17-134cd1815de8 · 28 evaluations · 28 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 |
|---|---|---|
| DNABERT-2 (further pre-trained on GUE) on GUE CORE-PROMOTER-DETECTION-ALL: Core promoter detection, dataset all Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE CORE-PROMOTER-DETECTION-ALL: Core promoter detection, dataset allDataset subset: GUE Core promoter detection, all (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 67.50% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Core promoter detection all) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE CORE-PROMOTER-DETECTION-NOTATA: Core promoter detection, dataset notata Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE CORE-PROMOTER-DETECTION-NOTATA: Core promoter detection, dataset notataDataset subset: GUE Core promoter detection, notata (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 69.53% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Core promoter detection notata) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE CORE-PROMOTER-DETECTION-TATA: Core promoter detection, dataset tata Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE CORE-PROMOTER-DETECTION-TATA: Core promoter detection, dataset tataDataset subset: GUE Core promoter detection, tata (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 76.18% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Core promoter detection tata) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE COVID-VARIANT-CLASSIFICATION-COVID: Covid variant classification, dataset Covid Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE COVID-VARIANT-CLASSIFICATION-COVID: Covid variant classification, dataset CovidDataset subset: GUE Covid variant classification, Covid (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 68.49% f1 Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Covid variant classification Covid) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3: Epigenetic marks prediction, dataset H3 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3: Epigenetic marks prediction, dataset H3Dataset subset: GUE Epigenetic marks prediction, H3 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 80.17% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K14AC: Epigenetic marks prediction, dataset H3K14ac Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K14AC: Epigenetic marks prediction, dataset H3K14acDataset subset: GUE Epigenetic marks prediction, H3K14ac (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 57.42% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3K14ac) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K36ME3: Epigenetic marks prediction, dataset H3K36me3 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K36ME3: Epigenetic marks prediction, dataset H3K36me3Dataset subset: GUE Epigenetic marks prediction, H3K36me3 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 61.90% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3K36me3) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME1: Epigenetic marks prediction, dataset H3K4me1 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME1: Epigenetic marks prediction, dataset H3K4me1Dataset subset: GUE Epigenetic marks prediction, H3K4me1 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 53.00% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3K4me1) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME2: Epigenetic marks prediction, dataset H3K4me2 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME2: Epigenetic marks prediction, dataset H3K4me2Dataset subset: GUE Epigenetic marks prediction, H3K4me2 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 39.89% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3K4me2) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME3: Epigenetic marks prediction, dataset H3K4me3 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME3: Epigenetic marks prediction, dataset H3K4me3Dataset subset: GUE Epigenetic marks prediction, H3K4me3 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 41.20% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3K4me3) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K79ME3: Epigenetic marks prediction, dataset H3K79me3 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K79ME3: Epigenetic marks prediction, dataset H3K79me3Dataset subset: GUE Epigenetic marks prediction, H3K79me3 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 65.46% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3K79me3) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K9AC: Epigenetic marks prediction, dataset H3K9ac Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H3K9AC: Epigenetic marks prediction, dataset H3K9acDataset subset: GUE Epigenetic marks prediction, H3K9ac (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 57.07% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H3K9ac) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H4: Epigenetic marks prediction, dataset H4 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H4: Epigenetic marks prediction, dataset H4Dataset subset: GUE Epigenetic marks prediction, H4 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 81.86% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H4) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H4AC: Epigenetic marks prediction, dataset H4ac Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE EPIGENETIC-MARKS-PREDICTION-H4AC: Epigenetic marks prediction, dataset H4acDataset subset: GUE Epigenetic marks prediction, H4ac (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 50.35% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Epigenetic marks prediction H4ac) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE PROMOTER-DETECTION-ALL: Promoter detection, dataset all Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE PROMOTER-DETECTION-ALL: Promoter detection, dataset allDataset subset: GUE Promoter detection, all (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 88.31% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Promoter detection all) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE PROMOTER-DETECTION-NOTATA: Promoter detection, dataset notata Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE PROMOTER-DETECTION-NOTATA: Promoter detection, dataset notataDataset subset: GUE Promoter detection, notata (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 94.34% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Promoter detection notata) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE PROMOTER-DETECTION-TATA: Promoter detection, dataset tata Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE PROMOTER-DETECTION-TATA: Promoter detection, dataset tataDataset subset: GUE Promoter detection, tata (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 68.79% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Promoter detection tata) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE SPLICE-SITE-PREDICTION-RECONSTRUCT: Splice site prediction, dataset Reconstruct Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE SPLICE-SITE-PREDICTION-RECONSTRUCT: Splice site prediction, dataset ReconstructDataset subset: GUE Splice site prediction, Reconstruct (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 85.93% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Splice site prediction Reconstruct) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-0: Transcription factor prediction (human), dataset 0 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-0: Transcription factor prediction (human), dataset 0Dataset subset: GUE Transcription factor prediction (human), 0 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 69.12% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Transcription factor prediction (human) 0) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-1: Transcription factor prediction (human), dataset 1 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-1: Transcription factor prediction (human), dataset 1Dataset subset: GUE Transcription factor prediction (human), 1 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 71.87% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Transcription factor prediction (human) 1) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-2: Transcription factor prediction (human), dataset 2 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-2: Transcription factor prediction (human), dataset 2Dataset subset: GUE Transcription factor prediction (human), 2 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 62.96% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Transcription factor prediction (human) 2) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-3: Transcription factor prediction (human), dataset 3 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-3: Transcription factor prediction (human), dataset 3Dataset subset: GUE Transcription factor prediction (human), 3 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 55.35% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Transcription factor prediction (human) 3) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-4: Transcription factor prediction (human), dataset 4 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-4: Transcription factor prediction (human), dataset 4Dataset subset: GUE Transcription factor prediction (human), 4 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 74.94% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Transcription factor prediction (human) 4) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-0: Transcription factor prediction (mouse), dataset 0 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-0: Transcription factor prediction (mouse), dataset 0Dataset subset: GUE Transcription factor prediction (mouse), 0 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 64.23% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Transcription factor prediction (mouse) 0) Source checking is not independent reproduction. |
| DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-1: Transcription factor prediction (mouse), dataset 1 Configuration: DNABERT-2 (further pre-trained on GUE)Task: GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-1: Transcription factor prediction (mouse), dataset 1Dataset subset: GUE Transcription factor prediction (mouse), 1 (GUE split) Fine-tuned on the GUE training split, scored on its test split. Split sizes are in Table 12. Author-reported evaluation · Evaluation metadata: source checked | ||
| 86.28% mcc Unit: percent · Direction: higher | Uncertainty: Not reported Scored: Not reported · Eligible: Not reported | source checkedDNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Table 6, row(DNABERT-2♦), column(Transcription factor prediction (mouse) 1) 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
- DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genomes · Original source · Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256
Technical metadata and extraction receipts
Stable ID: gue-method-dnabert-2-further-pre-trained-on-gue
- areas
- dna-genomes
- source locator
- Table 6, row(DNABERT-2♦)
- missing metadata
- checkpoint revision: unreported; parameters: unextracted
Related records
- model: DNABERT-2 (further pre-trained on GUE) on GUE CORE-PROMOTER-DETECTION-ALL: Core promoter detection, dataset all
- model: DNABERT-2 (further pre-trained on GUE) on GUE CORE-PROMOTER-DETECTION-NOTATA: Core promoter detection, dataset notata
- model: DNABERT-2 (further pre-trained on GUE) on GUE CORE-PROMOTER-DETECTION-TATA: Core promoter detection, dataset tata
- model: DNABERT-2 (further pre-trained on GUE) on GUE COVID-VARIANT-CLASSIFICATION-COVID: Covid variant classification, dataset Covid
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3: Epigenetic marks prediction, dataset H3
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K14AC: Epigenetic marks prediction, dataset H3K14ac
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K36ME3: Epigenetic marks prediction, dataset H3K36me3
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME1: Epigenetic marks prediction, dataset H3K4me1
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME2: Epigenetic marks prediction, dataset H3K4me2
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K4ME3: Epigenetic marks prediction, dataset H3K4me3
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K79ME3: Epigenetic marks prediction, dataset H3K79me3
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H3K9AC: Epigenetic marks prediction, dataset H3K9ac
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H4: Epigenetic marks prediction, dataset H4
- model: DNABERT-2 (further pre-trained on GUE) on GUE EPIGENETIC-MARKS-PREDICTION-H4AC: Epigenetic marks prediction, dataset H4ac
- model: DNABERT-2 (further pre-trained on GUE) on GUE PROMOTER-DETECTION-ALL: Promoter detection, dataset all
- model: DNABERT-2 (further pre-trained on GUE) on GUE PROMOTER-DETECTION-NOTATA: Promoter detection, dataset notata
- model: DNABERT-2 (further pre-trained on GUE) on GUE PROMOTER-DETECTION-TATA: Promoter detection, dataset tata
- model: DNABERT-2 (further pre-trained on GUE) on GUE SPLICE-SITE-PREDICTION-RECONSTRUCT: Splice site prediction, dataset Reconstruct
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-0: Transcription factor prediction (human), dataset 0
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-1: Transcription factor prediction (human), dataset 1
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-2: Transcription factor prediction (human), dataset 2
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-3: Transcription factor prediction (human), dataset 3
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-HUMAN-4: Transcription factor prediction (human), dataset 4
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-0: Transcription factor prediction (mouse), dataset 0
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-1: Transcription factor prediction (mouse), dataset 1
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-2: Transcription factor prediction (mouse), dataset 2
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-3: Transcription factor prediction (mouse), dataset 3
- model: DNABERT-2 (further pre-trained on GUE) on GUE TRANSCRIPTION-FACTOR-PREDICTION-MOUSE-4: Transcription factor prediction (mouse), dataset 4