ArticleBMC genomics2019
MRCNN: a deep learning model for regression of genome-wide DNA methylation.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 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
- DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.PLoS computational biology · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- UNet with Attention Networks: A Novel Deep Learning Approach for DNA Methylation Prediction in HeLa Cells.Genes · 2025Article
- CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.Genome biology · 2025Article
- CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites.Briefings in bioinformatics · 2024Article
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
- Predicting locus-specific DNA methylation levels in cancer and paracancer tissues.Epigenomics · 2024Article
- Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches.PloS one · 2024Article
- MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- On the prediction of non-CG DNA methylation using machine learning.NAR genomics and bioinformatics · 2023Article
- Article
- Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits.Genetic epidemiology · 2022Article
- Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes.Scientific reports · 2022Article
- Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers.Genes · 2022Article
- Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.The Plant cell · 2022Article
- Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells.Clinical and experimental pediatrics · 2022Article
- Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- A convolutional neural network-based regression model to infer the epigenetic crosstalk responsible for CG methylation patterns.BMC bioinformatics · 2021Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetermination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted biochemical mechanism of DNA methylation, obtaining a precise prediction is a considerably tough challenge. Existing approaches have yielded good predictions, but the methods either need to combine plenty of features and prerequisites or deal with only hypermethylation and hypomethylation.
resultsIn this paper, we propose a deep learning method for prediction of the genome-wide DNA methylation, in which the Methylation Regression is implemented by Convolutional Neural Networks (MRCNN). Through minimizing the continuous loss function, experiments show that our model is convergent and more precise than the state-of-art method (DeepCpG) according to results of the evaluation. MRCNN also achieves the discovery of de novo motifs by analysis of features from the training process.
conclusionsGenome-wide DNA methylation could be evaluated based on the corresponding local DNA sequences of target CpG loci. With the autonomous learning pattern of deep learning, MRCNN enables accurate predictions of genome-wide DNA methylation status without predefined features and discovers some de novo methylation-related motifs that match known motifs by extracting sequence patterns.
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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.