ArticleGenome biology2025
Evaluating the representational power of pre-trained DNA language models for regulatory genomics.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
- Review
- LAMBDA: a prophage detection benchmark for genomic language models.NAR genomics and bioinformatics · 2026Article
- Benchmarking biomedical foundation models.Nature methods · 2026Review
- GUANinE v1.1 reveals complementarity of supervised and genomic language models.Bioinformatics (Oxford, England) · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- xDecoder unlocks the potential of genomic foundation models for few-shot personal gene expression prediction.Molecular systems biology · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Review
- Evaluating DNA Function Understanding in Genomic Language Models Using Evolutionarily Implausible Sequences.ACS synthetic biology · 2026Article
- Transcription clusters and developmental pathways - nature, nurture, noise.Journal of cell science · 2026Review
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
- Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion.bioRxiv : the preprint server for biology · 2026Article
- LAMBDA: A Prophage Detection Benchmark for Genomic Language Models.bioRxiv : the preprint server for biology · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Short-Context Regulatory DNA Language Models with Motif-Discovery Regularization.bioRxiv : the preprint server for biology · 2026Article
- Probing genomic language models: Nucleotide Generative Pretrained Transformer and the role of pretraining in learned representations.Briefings in bioinformatics · 2026Article
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
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4 authors.
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Abstract
backgroundThe emergence of genomic language models (gLMs) offers an unsupervised approach to learning a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of functional activity generated by wet-lab experiments. Previous evaluations have shown that pre-trained gLMs can be leveraged to improve predictive performance across a broad range of regulatory genomics tasks, albeit using relatively simple benchmark datasets and baseline models. Since the gLMs in these studies were tested upon fine-tuning their weights for each downstream task, determining whether gLM representations embody a foundational understanding of cis-regulatory biology remains an open question.
resultsHere, we evaluate the representational power of pre-trained gLMs to predict and interpret cell-type-specific functional genomics data that span DNA and RNA regulation for six major functional genomics prediction tasks. Our findings suggest that probing the representations of current pre-trained gLMs do not offer substantial advantages over conventional machine learning approaches that use one-hot encoded sequences. Nevertheless, highly tuned supervised models trained from scratch using one-hot encoded sequences can achieve performance competitive with or better than pre-trained models across the datasets explored in this study. DISCUSSION: This work highlights a major gap with current gLMs, raising potential issues in conventional pre-training strategies for the non-coding genome.
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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.