Protocol

NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)

Classification of splice sites on the Nucleotide Transformer benchmark dataset, with every method given the same train/test split.

11 evaluations · 44 results

Overview

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

11 recorded evaluations, 44 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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Results

Results are available, but no reviewed comparison panel is linked in this release.

All evaluations

11 evaluations · 44 results. Different protocols are not a single leaderboard.

Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) · Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors

Sorted by Accuracy (NT-splice-sites-acceptors, fine-tuned) (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	Accuracy (NT-splice-sites-acceptors, fine-tuned)	F1 (NT-splice-sites-acceptors, fine-tuned)	Precision (NT-splice-sites-acceptors, fine-tuned)	Recall (NT-splice-sites-acceptors, fine-tuned)
NucleotideTransformer fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9899(0.0014) (best in column)	0.9899(0.0014) (best in column)	0.9899(0.0014) (best in column)	0.9900(0.0014) (best in column)
SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9868(0.0009)	0.9868(0.0009)	0.9868(0.0009)	0.9868(0.0009)
DNABERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9832(0.0011)	0.9832(0.0011)	0.9832(0.0011)	0.9832(0.0011)
RNAFM fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9796(0.0007)	0.9796(0.0007)	0.9796(0.0007)	0.9796(0.0007)
RNAErnie fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9794(0.0014)	0.9794(0.0014)	0.9794(0.0014)	0.9794(0.0014)
RNAMSM fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9784(0.0009)	0.9784(0.0009)	0.9784(0.0009)	0.9783(0.0009)
DNABERT2 fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9776(0.0005)	0.9776(0.0005)	0.9777(0.0005)	0.9776(0.0006)
UTRLM fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9693(0.0000)	0.9693(0.0000)	0.9695(0.0000)	0.9693(0.0000)
GENA-LM-large fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9618(0.0011)	0.9618(0.0011)	0.9621(0.0011)	0.9618(0.0012)
RNABERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9590(0.0005)	0.9590(0.0005)	0.9590(0.0005)	0.9590(0.0005)
GENA-LM-base fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	0.9545(0.0028)	0.9544(0.0028)	0.9555(0.0023)	0.9543(0.0029)

All 44 result rows with coverage, uncertainty and sources

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: DNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9832(0.0011) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0011 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT', column 'Accuracy'
Configuration: DNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9832(0.0011) F1 (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0011 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT', column 'F1 Score'
Configuration: DNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9832(0.0011) Precision (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0011 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT', column 'Precision'
Configuration: DNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9832(0.0011) Recall (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0011 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT', column 'Recall'
Configuration: DNABERT2 fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9776(0.0005) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT2 on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT2', column 'Accuracy'
Configuration: DNABERT2 fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9776(0.0005) F1 (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT2 on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT2', column 'F1 Score'
Configuration: DNABERT2 fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9777(0.0005) Precision (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT2 on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT2', column 'Precision'
Configuration: DNABERT2 fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9776(0.0006) Recall (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0006 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source DNABERT2 on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'DNABERT2', column 'Recall'
Configuration: GENA-LM-base fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9545(0.0028) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0028 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-base on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-base', column 'Accuracy'
Configuration: GENA-LM-base fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9544(0.0028) F1 (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0028 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-base on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-base', column 'F1 Score'
Configuration: GENA-LM-base fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9555(0.0023) Precision (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0023 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-base on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-base', column 'Precision'
Configuration: GENA-LM-base fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9543(0.0029) Recall (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0029 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-base on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-base', column 'Recall'
Configuration: GENA-LM-large fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9618(0.0011) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0011 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-large on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-large', column 'Accuracy'
Configuration: GENA-LM-large fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9618(0.0011) F1 (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0011 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-large on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-large', column 'F1 Score'
Configuration: GENA-LM-large fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9621(0.0011) Precision (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0011 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-large on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-large', column 'Precision'
Configuration: GENA-LM-large fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9618(0.0012) Recall (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0012 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM-large on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'GENA-LM-large', column 'Recall'
Configuration: NucleotideTransformer fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9899(0.0014) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NucleotideTransformer on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'NucleotideTransformer', column 'Accuracy'
Configuration: NucleotideTransformer fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9899(0.0014) F1 (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NucleotideTransformer on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'NucleotideTransformer', column 'F1 Score'
Configuration: NucleotideTransformer fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9899(0.0014) Precision (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NucleotideTransformer on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'NucleotideTransformer', column 'Precision'
Configuration: NucleotideTransformer fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9900(0.0014) Recall (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source NucleotideTransformer on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'NucleotideTransformer', column 'Recall'
Configuration: RNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9590(0.0005) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'RNABERT', column 'Accuracy'
Configuration: RNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9590(0.0005) F1 (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'RNABERT', column 'F1 Score'
Configuration: RNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9590(0.0005) Precision (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'RNABERT', column 'Precision'
Configuration: RNABERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9590(0.0005) Recall (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0005 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNABERT on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'RNABERT', column 'Recall'
Configuration: RNAErnie fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9794(0.0014) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNAErnie on NT-splice-sites-acceptors gfm2-20261011-you2026-protocol-nt-splice-acceptors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 4, block 'NT-splice-sites-acceptors', row 'RNAErnie', column 'Accuracy'

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Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications · Original source · Nat Commun 17:223, published online 2025-12-07; PMC12780105 full-text XML

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Technical metadata and extraction receipts

Stable ID: gfm2-20261011-you2026-protocol-nt-splice-acceptors

areas: dna-genomes
contexts: research
protocol: Fine-tune each model on the dataset's training split with an appended convolutional layer for the language models (sequence-level classification, Methods), then score the held-out test split. Each model is fine-tuned and tested three times with different random initialisations; mean and standard deviation are reported.
version: Supplementary Table 4, block 'NT-splice-sites-acceptors'
metric direction: higher
unit: fraction
aggregation: Mean over three fine-tuning runs
replicates: Three random initialisations
metric implementation: F1 score, precision, recall and accuracy as defined in Methods
limitations: Fine-tuned and tested three times with different random initialisations; the printed spread is the standard deviation over those three runs (Methods, 'To ensure the reliability of our findings').; Input length for the NT splice-site datasets is not stated in this source; the 500 bp division applies to the GTEx task.; Each language model is fine-tuned with an appended convolution head, so the comparison measures the adapted model, not zero-shot likelihood.; Values are read from the Supplementary Information tables behind the figures; the main-text figures are not read.
source locator: Supplementary Table 4, block 'NT-splice-sites-acceptors'; Table 1; Methods

Related records

uses data: Nucleotide Transformer benchmark splice-site dataset, acceptors
assessment: DNABERT on NT-splice-sites-acceptors
assessment: DNABERT2 on NT-splice-sites-acceptors
assessment: GENA-LM-base on NT-splice-sites-acceptors
assessment: GENA-LM-large on NT-splice-sites-acceptors
assessment: NucleotideTransformer on NT-splice-sites-acceptors
assessment: RNABERT on NT-splice-sites-acceptors
assessment: RNAErnie on NT-splice-sites-acceptors
assessment: RNAFM on NT-splice-sites-acceptors
assessment: RNAMSM on NT-splice-sites-acceptors
assessment: SpliceBERT on NT-splice-sites-acceptors
assessment: UTRLM on NT-splice-sites-acceptors

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