ArticleGenome biology2026
Fine-tuning sequence-to-expression models on personal genome and transcriptome data.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A scalable approach to investigating sequence-to-function predictions from personal genomes.Nature methods · 2026Article
- Fine-tuning sequence-to-expression models on personal genome and transcriptome data.Genome biology · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.bioRxiv : the preprint server for biology · 2026Article
- Personalized gene expression prediction in the era of deep learning: a review.Briefings in bioinformatics · 2026Review
- Deep-learning prediction of gene expression from personal genomes.Genome biology · 2026Article
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- Uncertainty-aware genomic deep learning with knowledge distillation.NPJ artificial intelligence · 2026Article
- Fine-tuning sequence to function deep learning models on large-scale proteomic data improves the accuracy of variant effect prediction.bioRxiv : the preprint server for biology · 2025Article
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.Research square · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Training deep learning models on personalized genomic sequences improves variant effect prediction.bioRxiv : the preprint server for biology · 2025Article
- GenVarLoader: An accelerated dataloader for applying deep learning to personalized genomics.bioRxiv : the preprint server for biology · 2025Article
- Uncertainty-aware genomic deep learning with knowledge distillation.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGenomic sequence-to-expression deep learning models, which are trained to predict gene expression and other molecular phenotypes across the reference genome, have recently been shown to have poor out-of-the-box performance in predicting gene expression variation across individuals based on their personal genome sequences.
resultsHere, we explore whether additional training (fine-tuning) on paired personal genome and transcriptome data improves the performance of such sequence-to-expression models. Using Enformer as a representative pre-trained model, we explore various fine-tuning strategies. Our results show that fine-tuning improves expression predictions on held-out individuals, including from held-out populations, for genes seen during fine-tuning, with comparable performance to variant-based linear models commonly used in transcriptome-wide association studies. However, fine-tuning does not improve model generalizability to held-out genes, which contain sequences and variants unseen during fine-tuning.
conclusionsIncluding individual-level genetic variation and paired expression data during the training of sequence-to-expression models improves their understanding of seen variants, enabling their application to held-out individuals. However, this strategy does not improve generalizability to unseen genes, highlighting a remaining open challenge in the field.
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