ArticleNature genetics2024
Interpreting cis-regulatory interactions from large-scale deep neural networks.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Predicting non-coding variant effects with AlphaGenome.Cell research · 2026Article
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- PoolParty: streamlined design of DNA sequence libraries in Python.bioRxiv : the preprint server for biology · 2026Article
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Deriving genetic codes for molecular phenotypes from first principles.bioRxiv : the preprint server for biology · 2026Article
- Widespread low-affinity motifs enhance chromatin accessibility and regulatory potential in mESCs.bioRxiv : the preprint server for biology · 2025Article
- Iterative improvement of deep learning models using synthetic regulatory genomics.Genome research · 2025Article
- gReLU: a comprehensive framework for DNA sequence modeling and design.Nature methods · 2025Article
- ETNet: an interpretable transformer framework for enhancer-enhancer interaction prediction with cross-context transferability.Briefings in bioinformatics · 2025Article
- Uncovering the Mechanistic Landscape of Regulatory DNA with Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology.International journal of molecular sciences · 2025Review
- Genome-wide rules of transcription factor cooperativity revealed throughbioRxiv : the preprint server for biology · 2025Article
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.Nucleic acids research · 2025Article
- ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants.bioRxiv : the preprint server for biology · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplicatively.Cell genomics · 2024Article
- Analysis of single-cell CRISPR perturbations indicates that enhancers act multiplicatively and provides limited evidence for epistatic-like interactions.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
- Update ofInterpreting2024
Authors and funding
2 authors.
Funding
Abstract
The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with orthogonal experimental data, providing insights into generalization but offering limited insights into their decision-making process. Existing model explainability tools focus mainly on motif analysis, which becomes complex when interpreting longer sequences. Here we present cis-regulatory element model explanations (CREME), an in silico perturbation toolkit that interprets the rules of gene regulation learned by a genomic DNN. Applying CREME to Enformer, a state-of-the-art DNN, we identify cis-regulatory elements that enhance or silence gene expression and characterize their complex interactions. CREME can provide interpretations across multiple scales of genomic organization, from cis-regulatory elements to fine-mapped functional sequence elements within them, offering high-resolution insights into the regulatory architecture of the genome. CREME provides a powerful toolkit for translating the predictions of genomic DNNs into mechanistic insights of gene regulation.
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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 OpenQuestion graph.