ArticleNature biotechnology2025
A community effort to optimize sequence-based deep learning models of gene regulation.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed.
- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
- Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.Genome research · 2026Article
- Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.Genes · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- Expression-linked promoter selection (ELiPS) engineers short, strong ubiquitous promoters for gene therapy applications.bioRxiv : the preprint server for biology · 2026Article
- Genome evolution through polyploidy: Enhancing plant stress resilience in agriculture.Proceedings of the National Academy of Sciences of the United States of America · 2026Review
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 2026Article
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- Predictive modeling of gene expression and localization of DNA binding site using deep convolutional neural networks.PLoS computational biology · 2026Article
- Identification of computationally designed skeletal and cardiac promoters with high specificity across AAV delivery routes.BMC biotechnology · 2026Article
- Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain.bioRxiv : the preprint server for biology · 2026Article
- Green bioconversion of insoluble chitin: chitinase development pathways via multi-strategy synergy.Bioresources and bioprocessing · 2026Review
- Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms.FEMS yeast research · 2026Review
- Uncertainty-aware genomic deep learning with knowledge distillation.NPJ artificial intelligence · 2026Article
- Distilling the Knowledge of Ensembles for Uncertainty-Aware Genomic Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Inferring binding specificities of human transcription factors with the wisdom of crowds.bioRxiv : the preprint server for biology · 2025Article
- Predicting dynamic expression patterns in budding yeast with a fungal DNA language model.Research square · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
26 authors.
Funding
Abstract
A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.