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| Bayesiánus egysejt-RNS-szekvenálás elemzés – Probabilis transzkriptomika× | Variációs Autoencoder× | |
|---|---|---|
| Tudományterület≠ | Bioinformatika | Mélytanulás |
| Módszercsalád≠ | Process / pipeline | Machine learning |
| Keletkezés éve≠ | 2018 (scVI landmark); Bayesian scRNA-seq approaches emerged 2015-2018 | 2014 |
| Megalkotó≠ | Romain Lopez, Nir Yosef and Michael I. Jordan (scVI framework); preceded by Bayesian single-cell methods from Kharchenko, Markowetz, and others | Kingma, D. P. & Welling, M. |
| Típus≠ | Probabilistic generative modeling pipeline | Deep generative latent-variable model (encoder–decoder) |
| Alapmű≠ | Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058. DOI ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Alternatív nevek | Bayesian scRNA-seq, scRNA-seq Bayesian modeling, probabilistic single-cell transcriptomics, Bayesian single-cell genomics | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Kapcsolódó≠ | 3 | 5 |
| Összefoglaló≠ | Bayesian single-cell RNA-seq analysis applies probabilistic generative models to the sparse, overdispersed count matrices produced by single-cell RNA sequencing. By placing prior distributions over latent biological variables — cell state, batch effects, dropout — the framework propagates uncertainty through every downstream inference step. Tools such as scVI, SCVI-tools, and BayesPrism implement this paradigm, enabling principled cell clustering, differential expression testing, and batch integration that explicitly models technical noise rather than ignoring it. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
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