ArticleGenome biology2017
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 244 papers, 1 of them a synthesis that pooled it.
What it found
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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
244 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- SMORE: joint dimension reduction and cell population discovery on single-cell methylome data.bioRxiv : the preprint server for biology · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Decoding the sequence determinants of locus-specific DNA methylation across human tissues.Nature communications · 2026Article
- The use of artificial intelligence in advancing molecular biology in Africa: a narrative review.Molecular genetics and genomics : MGG · 2026Review
- [Applications and Challenges of Deep Learning in Human Genome Research].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation.Briefings in bioinformatics · 2026Article
- An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.Bioinformatics (Oxford, England) · 2026Article
- DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.PLoS computational biology · 2026Article
- Epigenetic conditioning improves sequence-based modeling of gene regulation across cell types and alleles.bioRxiv : the preprint server for biology · 2026Article
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.Nature communications · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Immortal AI, Mortal Life: Long-Term Perspectives on AI and Human Knowledge.Journal of medical Internet research · 2026Article
- mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data.Nature communications · 2026Article
- Distinct cellular DNA methylation mechanisms underlie common and rare genetic risk for brain disorders.bioRxiv : the preprint server for biology · 2026Article
- Topological Entropy Correlates with the Predictive Power of Multiplexed Ensemble Reservoir Computing.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Recent advances in methodologies of epigenomics.Epigenomics · 2026Review
184 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability.
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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.