| Deep generative modeling for single-cell transcriptomics R Lopez, J Regier, MB Cole, MI Jordan, N Yosef Nature Methods 15 (12), 1053-1058, 2018 | 1529 | 2018 |
| Scrublet: computational identification of cell doublets in single-cell transcriptomic data SL Wolock, R Lopez, AM Klein Cell Systems, 357368, 2019 | 1474 | 2019 |
| A Python library for probabilistic analysis of single-cell omics data A Gayoso*, R Lopez*, G Xing*, P Boyeau, V Valiollah Pour Amiri, J Hong, ... Nature Biotechnology, 1-4, 2022 | 332* | 2022 |
| Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models C Xu*, R Lopez*, E Mehlman*, J Regier, MI Jordan, N Yosef Molecular Systems Biology, 2021 | 332* | 2021 |
| Joint probabilistic modeling of single-cell multi-omic data with totalVI A Gayoso*, Z Steier*, R Lopez, J Regier, KL Nazor, A Streets, N Yosef Nature Methods, 2021 | 319 | 2021 |
| Information Constraints on Auto-Encoding Variational Bayes R Lopez, J Regier, N Yosef, MI Jordan Advances in Neural Information Processing Systems, 2018 | 138 | 2018 |
| DestVI identifies continuums of cell types in spatial transcriptomics data R Lopez*, B Li*, H Keren-Shaul*, P Boyeau, M Kedmi, D Pilzer, A Jelinski, ... Nature Biotechnology, 1-10, 2022 | 125* | 2022 |
| A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements R Lopez*, A Nazaret*, M Langevin*, J Samaran*, J Regier*, MI Jordan, ... ICML workshop in Computational Biology, 2019 | 90 | 2019 |
| The scverse project provides a computational ecosystem for single-cell omics data analysis I Virshup, D Bredikhin, L Heumos, G Palla, G Sturm, A Gayoso, I Kats, ... Nature Biotechnology, 1-3, 2023 | 77 | 2023 |
| Enhancing scientific discoveries in molecular biology with deep generative models R Lopez, A Gayoso, N Yosef Molecular systems biology 16 (9), e9198, 2020 | 64 | 2020 |
| Decision-Making with Auto-Encoding Variational Bayes R Lopez, P Boyeau, N Yosef, MI Jordan, J Regier Advances in Neural Information Processing Systems, 2020 | 38 | 2020 |
| Large-Scale Differentiable Causal Discovery of Factor Graphs R Lopez, JC Hütter, JK Pritchard, A Regev Advances in Neural Information Processing Systems, 2022 | 27 | 2022 |
| Learning from eXtreme Bandit Feedback R Lopez, I Dhillon, MI Jordan AAAI Conference in Artificial Intelligence, 2021 | 25 | 2021 |
| Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling R Lopez, N Tagasovska, S Ra, K Cho, JK Pritchard, A Regev Causal Learning and Reasoning, 2023 | 23 | 2023 |
| A deep generative model for gene expression profiles from single-cell RNA sequencing R Lopez, J Regier, M Cole, M Jordan, N Yosef NeurIPS workshop in Computational Biology and Bay Area Machine Learning …, 2017 | 23* | 2017 |
| Cost-Effective Incentive Allocation via Structured Counterfactual Inference R Lopez, C Li, X Yan, J Xiong, MI Jordan, Y Qi, L Song AAAI Conference on Artificial Intelligence, 2020 | 22 | 2020 |
| Deep Generative Models for Detecting Differential Expression in Single Cells P Boyeau, R Lopez, J Regier, A Gayoso, MI Jordan, N Yosef Machine Learning in Computational Biology (MLCB) meeting, 2019 | 21* | 2019 |
| An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models P Boyeau, J Regier, A Gayoso, MI Jordan, R Lopez, N Yosef Proceedings of the National Academy of Sciences, 2023 | 19 | 2023 |
| Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data O Clivio, R Lopez, J Regier, A Gayoso, MI Jordan, N Yosef Machine Learning in Computational Biology (MLCB) meeting, 2019 | 12* | 2019 |
| NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning MG Sethuraman, R Lopez, R Mohan, F Fekri, T Biancalani, JC Hütter International Conference on Artificial Intelligence and Statistics, 2023 | 10 | 2023 |
Nir YosefAssociate Professor, Department of Systems Immunology, Weizmann Institute of ScienceVerified email at berkeley.edu
