ArticleScientific reports2016
Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.PLoS computational biology · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- TCN-5mC: a predictor of 5-methylcytosine sites based on multi-feature fusion and TCN-inspired block networks.Frontiers in genetics · 2026Article
- Genomics and Epigenomics Approaches for the Quantification of Circulating Tumor DNA in Liquid Biopsy: Relevance of a Multimodal Strategy.International journal of molecular sciences · 2025Review
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- GPX4 Promoter Hypermethylation Induced by Ischemia/Reperfusion Injury Regulates Hepatocytic Ferroptosis.Journal of clinical and translational hepatology · 2024Article
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
- Learning Micro-C from Hi-C with diffusion models.PLoS computational biology · 2024Article
- From tradition to innovation: conventional and deep learning frameworks in genome annotation.Briefings in bioinformatics · 2024Review
- Seq-RBPPred: Predicting RNA-Binding Proteins from Sequence.ACS omega · 2024Article
- i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.BMC genomics · 2024Article
- PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms.Frontiers in genetics · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM.Briefings in bioinformatics · 2023Article
- scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data.Briefings in bioinformatics · 2023Article
- Recall DNA methylation levels at low coverage sites using a CNN model in WGBS.PLoS computational biology · 2023Article
- DFHiC: a dilated full convolution model to enhance the resolution of Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.Genomics, proteomics & bioinformatics · 2022Article
- Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers.Genes · 2022Article
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Corrections and comments
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.
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