ArticleBMC bioinformatics2020
methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 17 citations in OpenAlex.
- SCONE: a subset-contrastive method for multi-omics network embedding.Briefings in bioinformatics · 2026Article
- Deep Learning-Based DNA Methylation Detection in Cervical Cancer Using the One-Hot Character Representation Technique.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Generative artificial intelligence model for simulating structural brain changes in schizophrenia.Frontiers in psychiatry · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles.BMC bioinformatics · 2023Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundRecently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylation signatures in cancer, demand for utilizing publicly available methylome dataset has been increased. To satisfy this, large-scale projects were launched to discover biological insights into cancer, providing a collection of the dataset. However, public cancer data, especially for certain cancer types, is still limited to be used in research. Several simulation tools for producing epigenetic dataset have been introduced in order to alleviate the issue, still, to date, generation for user-specified cancer type dataset has not been proposed.
resultsIn this paper, we present methCancer-gen, a tool for generating DNA methylome dataset considering type for cancer. Employing conditional variational autoencoder, a neural network-based generative model, it estimates the conditional distribution with latent variables and data, and generates samples for specified cancer type.
conclusionsTo evaluate the simulation performance of methCancer-gen for the user-specified cancer type, our proposed model was compared to a benchmark method and it could successfully reproduce cancer type-wise data with high accuracy helping to alleviate the lack of condition-specific data issue. methCancer-gen is publicly available at https://github.com/cbi-bioinfo/methCancer-gen.
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