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README.md

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+---
+language:
+- en
+metrics:
+- accuracy
+- AUC ROC
+- precision
+- recall
+tags:
+- biology
+- chemistry
+- therapeutic science
+- drug design
+- drug development
+- therapeutics
+library_name: tdc
+license: bsd-2-clause
+---
+COMING SOON
+weights extracted from https://cellxgene.cziscience.com/census-models
+
+## Model description
+Single-cell variational inference (scVI) is a powerful tool for the probabilistic analysis of single-cell transcriptomics data. It uses deep generative models to address technical noise and batch effects, providing a robust framework for various downstream analysis tasks. 
+To load the pre-trained model, use the Files and Versions tab files.
+
+## References
+* Lopez, R., Regier, J., Cole, M., Jordan, M. I., & Yosef, N. (2018). Deep Generative Modeling for Single-cell Transcriptomics. Nature Methods, 15, 1053-1058.
+* Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., Liu, Y., Samaran, J., Misrachi, G., Nazaret, A., Clivio, O., Xu, C. A., Ashuach, T., Lotfollahi, M., Svensson, V., Beltrame, E., Talavera-López, C., ... Yosef, N. (2021). scvi-tools: a library for deep probabilistic analysis of single-cell omics data. bioRxiv.
+* CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data CZI Single-Cell Biology, et al. bioRxiv 2023.10.30; doi: https://doi.org/10.1101/2023.10.30.563174

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