ArticleNucleic acids research2016
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 373 papers, 1 of them a synthesis that pooled it.
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Who cites it
373 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.Briefings in bioinformatics · 2023Pooled it
- PROTACs in cancer therapy: targeted degradation of GPX4, PARP and epigenetic regulators.Journal of enzyme inhibition and medicinal chemistry · 2026Review
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- Unlocking the Regulatory Genome: Interpreting the Clinical Impact of Noncoding Variants in Genetic Cardiomyopathies.Circulation. Genomic and precision medicine · 2026Review
- GenART: Reading the genome's language with adaptive "words".Cell reports methods · 2026Article
- BiMSGP: A Bidirectional Mamba-Based Model for Scalable and Accurate Genomic Prediction in Plants.Genes · 2026Article
- An atlas of transcription factor cooperation reveals how motif readers shape regulatory output.bioRxiv : the preprint server for biology · 2026Article
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- Identification and splicing assessment of a novel homozygous CFAP65 splice-site variant associated with oligozoospermia and complete asthenozoospermia.BMC medical genomics · 2026Article
- Cross-Model Uncertainty-Aware Minimal Editing of Cis-Regulatory Elements for Cell-Type-Selective Design.Genes · 2026Article
- DDTRN: predicting bacterial transcriptional regulatory networks based on gene sequences using dual descriptor.Briefings in bioinformatics · 2026Article
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- DeepGeSeq: deep learning library for genomic sequence modeling and analysis.Bioinformatics (Oxford, England) · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.Nucleic acids research · 2026Article
- A chromatin-structure-guided framework for predictive and interpretable regulatory genomics.Briefings in bioinformatics · 2026Article
- Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction.NAR genomics and bioinformatics · 2026Article
- Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.bioRxiv : the preprint server for biology · 2026Article
- CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.Nature methods · 2026Article
- Efficient and interpretable DNA/RNA representation using Komlós-Hadamard transforms.BMC bioinformatics · 2026Article
313 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.
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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 OpenQuestion graph.