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 40 (2), 163-166, 2022 | 248 | 2022 |
Robust federated learning in a heterogeneous environment A Ghosh, J Hong, D Yin, K Ramchandran arXiv preprint arXiv:1906.06629, 2019 | 211 | 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 41 (5), 604-606, 2023 | 50 | 2023 |
Likelihood-based deconvolution of bulk gene expression data using single-cell references DD Erdmann-Pham, J Fischer, J Hong, YS Song Genome research 31 (10), 1794-1806, 2021 | 26 | 2021 |
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells A Gayoso, P Weiler, M Lotfollahi, D Klein, J Hong, A Streets, FJ Theis, ... Nature methods 21 (1), 50-59, 2024 | 19 | 2024 |
Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics P Boyeau, J Hong, A Gayoso, MI Jordan, E Azizi, N Yosef BioRxiv, 2022.10. 04.510898, 2022 | 8 | 2022 |
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data M Chevalley, J Sackett-Sanders, Y Roohani, P Notin, A Bakulin, ... arXiv preprint arXiv:2308.15395, 2023 | 3 | 2023 |
Stable Differentiable Causal Discovery A Nazaret, J Hong, E Azizi, D Blei arXiv preprint arXiv:2311.10263, 2023 | | 2023 |
BetterBoost-Inference of Gene Regulatory Networks with Perturbation Data A Nazaret, J Hong | | 2023 |
CRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue J Gu, A Iyer, B Wesley, A Taglialatela, G Leuzzi, S Hangai, A Decker, ... bioRxiv, 2023.12. 26.572587, 2023 | | 2023 |
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells D Klein, J Hong, A Streets, FJ Theis, N Yosef | | |