ArticleBioinformatics (Oxford, England)2020
Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data.Briefings in bioinformatics · 2025Article
- Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.Science advances · 2025Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.bioRxiv : the preprint server for biology · 2024Article
- AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects.Bioinformatics (Oxford, England) · 2024Article
- ctGAN: combined transformation of gene expression and survival data with generative adversarial network.Briefings in bioinformatics · 2024Article
- Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Ten quick tips for avoiding pitfalls in multi-omics data integration analyses.PLoS computational biology · 2023Article
- Predicting cellular responses to complex perturbations in high-throughput screens.Molecular systems biology · 2023Article
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
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7 authors.
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Abstract
motivationThe transcriptomic data are being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are the data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details about the samples which we would like to control (gender, biological state, treatment, etc.) can be used as style components.
resultsThe proposed style transfer solution is based on Conditional Variational Autoencoders, Y-Autoencoders and adversarial feature decomposition. To quantitatively measure the quality of the style transfer, neural network classifiers which predict the style and semantics after training on real expression were used. Comparison with several existing style-transfer based approaches shows that proposed model has the highest style prediction accuracy on all considered datasets while having comparable or the best semantics prediction accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/NRshka/stvae-source. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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