ArticleGigaScience2020
Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies.Genome biology · 2026Article
- Unified imputation of missing data modalities and features in multi-omic data via shared representation learning.bioRxiv : the preprint server for biology · 2026Article
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
- SynOmics: integrating multi-omics data through feature interaction networks.Briefings in bioinformatics · 2025Article
- scHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data.BMC bioinformatics · 2025Article
- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- Review
- Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates.Computational and structural biotechnology journal · 2025Article
- Optimizing multi-omics data imputation with NMF and GAN synergy.Bioinformatics (Oxford, England) · 2024Article
- CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.Computers in biology and medicine · 2024Article
- Data Augmentation with Cross-Modal Variational Autoencoders (DACMVA) for Cancer Survival Prediction.Information (Basel) · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- Deep Learning Methods for Omics Data Imputation.Biology · 2023Review
- CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data.Research square · 2023Article
- Article
- Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.Briefings in bioinformatics · 2023Article
- Missing data in multi-omics integration: Recent advances through artificial intelligence.Frontiers in artificial intelligence · 2023Review
- Article
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets.
resultsHere, we have developed a novel method to impute missing gene expression data from DNA methylation data through a transfer learning-based neural network, namely, TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with a 7-11% improvement under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation-driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purposes by an independent test on the Wilms tumor dataset from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project.
conclusionsTDimpute is an effective method for RNA-seq imputation with limited training samples.
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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.