| 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 | |
| The TDC Transformers API is still under development. You may download scVI pre-trained weights and hyperparameters from the files included. Files sourced from scvi-tools huggingface (https://huggingface.co/scvi-tools). | |
| ## 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. |