ArticleGigaScience2020
Genomic data imputation with variational auto-encoders.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 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
- NExON-Bayes: a Bayesian approach to network estimation informed by ordinal covariates.Bioinformatics (Oxford, England) · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Clinically interpretable deep learning for breast cancer missense variant pathogenicity prediction.Frontiers in bioinformatics · 2026Article
- Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.Briefings in bioinformatics · 2025Article
- Exposure-inducible genes may contribute to missingness in RNAseq-based gene expression analyses.Scientific reports · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- One-sample missing DNA-methylation value imputation.BMC bioinformatics · 2025Article
- Imputing single-cell protein abundance in multiplex tissue imaging.Nature communications · 2025Article
- CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.Genome biology · 2025Article
- Flexible imputation toolkit for electronic health records.Scientific reports · 2025Article
- A Robust Multivariate Time Series Classification Approach Based on Topological Data Analysis for Channel Fault Tolerance.Sensors (Basel, Switzerland) · 2025Article
- Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models.Nature biomedical engineering · 2025Article
- Unsupervised data imputation with multiple importance sampling variational autoencoders.Scientific reports · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- DUNE: a versatile neuroimaging encoder captures brain complexity across 3 major diseases: cancer, dementia, and schizophrenia.GigaScience · 2025Article
- Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.PloS one · 2025Article
- A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis.Frontiers in medicine · 2025Review
- GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs.Computational and structural biotechnology journal · 2024Article
- A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundAs missing values are frequently present in genomic data, practical methods to handle missing data are necessary for downstream analyses that require complete data sets. State-of-the-art imputation techniques, including methods based on singular value decomposition and K-nearest neighbors, can be computationally expensive for large data sets and it is difficult to modify these algorithms to handle certain cases not missing at random.
resultsIn this work, we use a deep-learning framework based on the variational auto-encoder (VAE) for genomic missing value imputation and demonstrate its effectiveness in transcriptome and methylome data analysis. We show that in the vast majority of our testing scenarios, VAE achieves similar or better performances than the most widely used imputation standards, while having a computational advantage at evaluation time. When dealing with data missing not at random (e.g., few values are missing), we develop simple yet effective methodologies to leverage the prior knowledge about missing data. Furthermore, we investigate the effect of varying latent space regularization strength in VAE on the imputation performances and, in this context, show why VAE has a better imputation capacity compared to a regular deterministic auto-encoder.
conclusionsWe describe a deep learning imputation framework for transcriptome and methylome data using a VAE and show that it can be a preferable alternative to traditional methods for data imputation, especially in the setting of large-scale data and certain missing-not-at-random scenarios.
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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.