Dataset

Nucleotide Transformer benchmark splice-site dataset, acceptors

Acceptor-site classification data covering 147 species and 2 categories: 22,179 sequences, 19,961 train and 2,218 test.

Evaluation results

11 evaluations · 44 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 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'

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

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Release 2026-10-22-1b7cf027e4f6 · Evidence verified: Not verified

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Verified: Not verified

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Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

14 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Population 147 species, 2 categories; 22,179 sequences Context-only references	Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nat Commun 17:223, published online 2025-12-07; PMC12780105 full-text XML Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.population` Source artifact SHA-256: `2c6b2816846a21296c56b4dd58f3d1e8810e9d96e224d3ab21b5eb8faa9928e4` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Population 147 species, 2 categories; 22,179 sequences Context-only references	You et al. 2026, Supplementary Information Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 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) Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.population` Source artifact SHA-256: `cbb69e05045a4526e35479d2c1361ebb9cfadfd968e00675390dad597035d882` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Context-only references	Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nat Commun 17:223, published online 2025-12-07; PMC12780105 full-text XML Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `2c6b2816846a21296c56b4dd58f3d1e8810e9d96e224d3ab21b5eb8faa9928e4` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Context-only references	You et al. 2026, Supplementary Information Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 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) Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `cbb69e05045a4526e35479d2c1361ebb9cfadfd968e00675390dad597035d882` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Split 19,961 train / 2,218 test Context-only references	Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nat Commun 17:223, published online 2025-12-07; PMC12780105 full-text XML Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.split` Source artifact SHA-256: `2c6b2816846a21296c56b4dd58f3d1e8810e9d96e224d3ab21b5eb8faa9928e4` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Split 19,961 train / 2,218 test Context-only references	You et al. 2026, Supplementary Information Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 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) Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.split` Source artifact SHA-256: `cbb69e05045a4526e35479d2c1361ebb9cfadfd968e00675390dad597035d882` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Total 22179 Context-only references	Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nat Commun 17:223, published online 2025-12-07; PMC12780105 full-text XML Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.total` Source artifact SHA-256: `2c6b2816846a21296c56b4dd58f3d1e8810e9d96e224d3ab21b5eb8faa9928e4` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Total 22179 Context-only references	You et al. 2026, Supplementary Information Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 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) Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.total` Source artifact SHA-256: `cbb69e05045a4526e35479d2c1361ebb9cfadfd968e00675390dad597035d882` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Version As used by You et al. 2026 (Table 1, 'NT-splice-sites-acceptors') Context-only references	Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nat Commun 17:223, published online 2025-12-07; PMC12780105 full-text XML Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.version` Source artifact SHA-256: `2c6b2816846a21296c56b4dd58f3d1e8810e9d96e224d3ab21b5eb8faa9928e4` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Version As used by You et al. 2026 (Table 1, 'NT-splice-sites-acceptors') Context-only references	You et al. 2026, Supplementary Information Original source ↗ Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 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) Retrieved: 2026-10-11T00:55:34Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.version` Source artifact SHA-256: `cbb69e05045a4526e35479d2c1361ebb9cfadfd968e00675390dad597035d882` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Sources and history

Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

2 source records and release history

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-data-nt-splice-acceptors

areas: dna-genomes
contexts: research
version: As used by You et al. 2026 (Table 1, 'NT-splice-sites-acceptors')
split: 19,961 train / 2,218 test
population: 147 species, 2 categories; 22,179 sequences
total: 22179
source locator: Table 1, 'Splicing prediction', row 'NT-splice-sites-acceptors'

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