SpTransformer retrained under the authors' framework (You et al. 2026)
SpTransformer retrained under the authors' framework: Task-specific method retrained on each benchmark training split under the authors' PyTorch framework, with non-PyTorch methods reimplemented in PyTorch; GTEx task input divided into 500 bp blocks; trained and tested three times with different random initialisations.
Overview
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Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
3 evaluations · 3 results. Different protocols are not a single leaderboard.
Dataset: GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)
Sorted by AUPRC (GTEx splicing task, Splice acceptors and donors, averaged over categories) (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 | 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) |
|---|---|---|---|---|
| SpTransformer retrained under the authors' framework (You et al. 2026)Evaluation | GTEx tissue-specific splicing prediction, Splice acceptors and donors (You et al. 2026 Supplementary Table 5) | 0.8841(0.0018) | Not reported | Not reported |
| SpTransformer retrained under the authors' framework (You et al. 2026)Evaluation | GTEx tissue-specific splicing prediction, Splice-15class (You et al. 2026 Supplementary Table 5) | Not reported | 0.8568(0.0005) | Not reported |
| SpTransformer retrained under the authors' framework (You et al. 2026)Evaluation | GTEx tissue-specific splicing prediction, Splice-53class (You et al. 2026 Supplementary Table 5) | Not reported | Not reported | 0.8552(0.0053) |
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How it works, versions and access
Strengths, limitations and unresolved questions
Evidence
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Evidence table
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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)
Technical metadata and extraction receipts
Stable ID: gfm2-20261011-you2026-config-sptransformer
- areas
- dna-genomes
- contexts
- research
- method types
- specialist
- reported name
- SpTransformer
- foundation model eligible
- false
- source locator
- Methods, 'Construction of tissue-specific alternative splicing prediction task'; Supplementary Tables 4 and 5, row 'SpTransformer'
- parameters
- Task-specific method retrained on each benchmark training split under the authors' PyTorch framework, with non-PyTorch methods reimplemented in PyTorch (Methods); GTEx task input divided into 500 bp blocks; trained 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
- Retrained from scratch on the task training split
- provenance note
- SpTransformer retrained under the authors' framework (You et al. 2026), as evaluated in the cited comparison.