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SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)

SpliceBERT fine-tuned with an appended convolutional layer: Officially released pre-trained weights fine-tuned per task with a convolutional layer appended to the backbone; AdamW; GTEx task input divided into 500 bp blocks.

6 evaluations · 15 results

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Evaluations and results

6 evaluations · 15 results. Different protocols are not a single leaderboard.

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	Protocol and dataset	Accuracy (NT-splice-sites-acceptors, fine-tuned)	Accuracy (NT-splice-sites-all, fine-tuned)	Accuracy (NT-splice-sites-donors, fine-tuned)	AUPRC (GTEx splicing task, Splice acceptors and donors, averaged over categories)	AUPRC (GTEx splicing task, Splice-15class, averaged over categories)	AUPRC (GTEx splicing task, Splice-53class, averaged over categories)	F1 (NT-splice-sites-acceptors, fine-tuned)	F1 (NT-splice-sites-all, fine-tuned)	F1 (NT-splice-sites-donors, fine-tuned)	Precision (NT-splice-sites-acceptors, fine-tuned)	Precision (NT-splice-sites-all, fine-tuned)	Precision (NT-splice-sites-donors, fine-tuned)	Recall (NT-splice-sites-acceptors, fine-tuned)	Recall (NT-splice-sites-all, fine-tuned)	Recall (NT-splice-sites-donors, fine-tuned)
SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4), Nucleotide Transformer benchmark splice-site dataset, acceptors	0.9868(0.0009)	Not reported	Not reported	Not reported	Not reported	Not reported	0.9868(0.0009)	Not reported	Not reported	0.9868(0.0009)	Not reported	Not reported	0.9868(0.0009)	Not reported	Not reported
SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4), Nucleotide Transformer benchmark splice-site dataset, all sites	Not reported	0.9861(0.0014)	Not reported	Not reported	Not reported	Not reported	Not reported	0.9861(0.0014)	Not reported	Not reported	0.9862(0.0013)	Not reported	Not reported	0.9861(0.0014)	Not reported
SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4), Nucleotide Transformer benchmark splice-site dataset, donors	Not reported	Not reported	0.9915(0.0010)	Not reported	Not reported	Not reported	Not reported	Not reported	0.9915(0.0010)	Not reported	Not reported	0.9915(0.0009)	Not reported	Not reported	0.9915(0.0010)
SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	GTEx tissue-specific splicing prediction, Splice acceptors and donors (You et al. 2026 Supplementary Table 5), GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	Not reported	Not reported	Not reported	0.8317(0.0096)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	GTEx tissue-specific splicing prediction, Splice-15class (You et al. 2026 Supplementary Table 5), GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	Not reported	Not reported	Not reported	Not reported	0.7538(0.0014)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported
SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)Evaluation	GTEx tissue-specific splicing prediction, Splice-53class (You et al. 2026 Supplementary Table 5), GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	Not reported	Not reported	Not reported	Not reported	Not reported	0.7387(0.0020)	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported	Not reported

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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: SpliceBERT 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	0.9868(0.0009) Accuracy (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0009 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT 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 'SpliceBERT', column 'Accuracy'
Configuration: SpliceBERT 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	0.9868(0.0009) F1 (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0009 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT 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 'SpliceBERT', column 'F1 Score'
Configuration: SpliceBERT 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	0.9868(0.0009) Precision (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0009 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT 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 'SpliceBERT', column 'Precision'
Configuration: SpliceBERT 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	0.9868(0.0009) Recall (NT-splice-sites-acceptors, fine-tuned) fraction · higher Uncertainty: SD 0.0009 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT 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 'SpliceBERT', column 'Recall'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, all sites	0.9861(0.0014) Accuracy (NT-splice-sites-all, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-all gfm2-20261011-you2026-protocol-nt-splice-all 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-all', row 'SpliceBERT', column 'Accuracy'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, all sites	0.9861(0.0014) F1 (NT-splice-sites-all, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-all gfm2-20261011-you2026-protocol-nt-splice-all 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-all', row 'SpliceBERT', column 'F1 Score'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, all sites	0.9862(0.0013) Precision (NT-splice-sites-all, fine-tuned) fraction · higher Uncertainty: SD 0.0013 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-all gfm2-20261011-you2026-protocol-nt-splice-all 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-all', row 'SpliceBERT', column 'Precision'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, all sites	0.9861(0.0014) Recall (NT-splice-sites-all, fine-tuned) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-all gfm2-20261011-you2026-protocol-nt-splice-all 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-all', row 'SpliceBERT', column 'Recall'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, donors	0.9915(0.0010) Accuracy (NT-splice-sites-donors, fine-tuned) fraction · higher Uncertainty: SD 0.0010 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-donors gfm2-20261011-you2026-protocol-nt-splice-donors 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-donors', row 'SpliceBERT', column 'Accuracy'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, donors	0.9915(0.0010) F1 (NT-splice-sites-donors, fine-tuned) fraction · higher Uncertainty: SD 0.0010 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-donors gfm2-20261011-you2026-protocol-nt-splice-donors 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-donors', row 'SpliceBERT', column 'F1 Score'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, donors	0.9915(0.0009) Precision (NT-splice-sites-donors, fine-tuned) fraction · higher Uncertainty: SD 0.0009 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-donors gfm2-20261011-you2026-protocol-nt-splice-donors 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-donors', row 'SpliceBERT', column 'Precision'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4) Dataset: Nucleotide Transformer benchmark splice-site dataset, donors	0.9915(0.0010) Recall (NT-splice-sites-donors, fine-tuned) fraction · higher Uncertainty: SD 0.0010 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on NT-splice-sites-donors gfm2-20261011-you2026-protocol-nt-splice-donors 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-donors', row 'SpliceBERT', column 'Recall'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: GTEx tissue-specific splicing prediction, Splice acceptors and donors (You et al. 2026 Supplementary Table 5) Dataset: GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	0.8317(0.0096) AUPRC (GTEx splicing task, Splice acceptors and donors, averaged over categories) fraction · higher Uncertainty: SD 0.0096 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on the GTEx splicing task, Splice acceptors and donors gfm2-20261011-you2026-protocol-gtex-acceptors-donors Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 5, block 'Splice acceptors and donors', row 'SpliceBERT', column 'Average PR-AUC'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: GTEx tissue-specific splicing prediction, Splice-15class (You et al. 2026 Supplementary Table 5) Dataset: GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	0.7538(0.0014) AUPRC (GTEx splicing task, Splice-15class, averaged over categories) fraction · higher Uncertainty: SD 0.0014 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on the GTEx splicing task, Splice-15class gfm2-20261011-you2026-protocol-gtex-splice-15class Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 5, block 'Splice-15class', row 'SpliceBERT', column 'Average PR-AUC'
Configuration: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	Protocol: GTEx tissue-specific splicing prediction, Splice-53class (You et al. 2026 Supplementary Table 5) Dataset: GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	0.7387(0.0020) AUPRC (GTEx splicing task, Splice-53class, averaged over categories) fraction · higher Uncertainty: SD 0.0020 Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source SpliceBERT on the GTEx splicing task, Splice-53class gfm2-20261011-you2026-protocol-gtex-splice-53class Aggregation: Not reported Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information · Supplementary Table 5, block 'Splice-53class', row 'SpliceBERT', column 'Average PR-AUC'

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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
You et al. 2026, Supplementary Information · Original source · 41467_2025_66899_MOESM1_ESM.pdf inside the Europe PMC supplementaryFiles zip (outer zip 9a0d0a8d8643872ab265e3e6ed7eea944563da4861bf764f97c68fc6aa502525, assembled per request; the member hash is the bound artifact)

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Technical metadata and extraction receipts

Stable ID: gfm2-20261011-you2026-config-splicebert

areas: dna-genomes
contexts: research
method types: foundation_model
reported name: SpliceBERT
foundation model eligible: true
source locator: Methods, 'Construction of tissue-specific alternative splicing prediction task'; Supplementary Tables 4 and 5, row 'SpliceBERT'
parameters: Officially released pre-trained weights fine-tuned per task with a convolutional layer appended to the backbone (Methods); AdamW; parameters in Supplementary Table 1; GTEx task input divided into 500 bp blocks; fine-tuned and tested three times with different random initialisations
missing metadata: version: reason: unreported; note: Checkpoint revision not printed; fine-tuning parameters are in Supplementary Table 1
training setting: Fine-tuned on the task training split
provenance note: SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026), as evaluated in the cited comparison.

Related records

configuration of: SpliceBERT
system: SpliceBERT on NT-splice-sites-acceptors
system: SpliceBERT on NT-splice-sites-all
system: SpliceBERT on NT-splice-sites-donors
system: SpliceBERT on the GTEx splicing task, Splice acceptors and donors
system: SpliceBERT on the GTEx splicing task, Splice-15class
system: SpliceBERT on the GTEx splicing task, Splice-53class

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