ArticlePLoS computational biology2020
Cross-species regulatory sequence activity prediction.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers, 1 of them a synthesis that pooled it.
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Who cites it
138 citing papers in PubMed, 1 synthesis or guideline pooled it, 238 citations in OpenAlex.
- The genetic and biochemical determinants of mRNA degradation rates in mammals.Genome biology · 2022Pooled it
- DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features.PLoS computational biology · 2026Article
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- ContiTE: continuous manifold MoE for few-shot cross-tissue mRNA translation efficiency prediction.Briefings in bioinformatics · 2026Article
- Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.Bioinformatics (Oxford, England) · 2026Article
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- Engineering monosex and sterile fish for food production: from conventional methods to CRISPR-based precision.Advanced biotechnology · 2026Review
- Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina.Science advances · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.Nucleic acids research · 2026Article
- Functional motif detection via in silico ablation using AlphaGenome.Briefings in bioinformatics · 2026Article
- A chromatin-structure-guided framework for predictive and interpretable regulatory genomics.Briefings in bioinformatics · 2026Article
- Multiple instance fine-mapping: Predicting causal regulatory variants with a deep sequence model.PLoS genetics · 2026Article
- Challenges in predicting chromatin accessibility differences between species.NAR genomics and bioinformatics · 2026Article
- Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes.bioRxiv : the preprint server for biology · 2026Article
- Large-scale data-driven pre-trained DNA models enhance performance across diverse genomics tasks.Nature communications · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.bioRxiv : the preprint server for biology · 2026Article
- Refining sequence-to-expression modelling with chromatin accessibility.Bioinformatics (Oxford, England) · 2026Article
- Greedy regression-based cross-species gene expression modeling: a translational framework for forensic wound age estimation.International journal of legal medicine · 2026Article
78 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, model organisms have been less explored. Model organism genomes offer both additional training sequences and unique annotations describing tissue and cell states unavailable in humans. Here, we develop a strategy to train deep convolutional neural networks simultaneously on multiple genomes and apply it to learn sequence predictors for large compendia of human and mouse data. Training on both genomes improves gene expression prediction accuracy on held out and variant sequences. We further demonstrate a novel and powerful approach to apply mouse regulatory models to analyze human genetic variants associated with molecular phenotypes and disease. Together these techniques unleash thousands of non-human epigenetic and transcriptional profiles toward more effective investigation of how gene regulation affects human disease.
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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.