| Deep packet: A novel approach for encrypted traffic classification using deep learning M Lotfollahi, MJ Siavoshani, RSH Zade, M Saberian Soft Computing, 1-14, 2019 | 930 | 2019 |
| Squidpy: a scalable framework for spatial omics analysis G Palla, H Spitzer, M Klein, D Fischer, AC Schaar, LB Kuemmerle, ... Nature methods 19 (2), 171-178, 2022 | 405* | 2022 |
| scGen predicts single-cell perturbation responses M Lotfollahi, FA Wolf, FJ Theis Nature methods 16 (8), 715-721, 2019 | 346 | 2019 |
| Mapping single-cell data to reference atlases by transfer learning M Lotfollahi, M Naghipourfar, MD Luecken, M Khajavi, M Büttner, ... Nature biotechnology 40 (1), 121-130, 2022 | 333* | 2022 |
| 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 | 288 | 2022 |
| Best practices for single-cell analysis across modalities L Heumos, AC Schaar, C Lance, A Litinetskaya, F Drost, L Zappia, ... Nature Reviews Genetics 24 (8), 550-572, 2023 | 263 | 2023 |
| An integrated cell atlas of the lung in health and disease L Sikkema, C Ramírez-Suástegui, DC Strobl, TE Gillett, L Zappia, ... Nature Medicine 29 (6), 1563-1577, 2023 | 226* | 2023 |
| Predicting cellular responses to complex perturbations in high‐throughput screens M Lotfollahi, A Klimovskaia Susmelj, C De Donno, L Hetzel, Y Ji, IL Ibarra, ... Molecular Systems Biology, e11517, 2023 | 108* | 2023 |
| Conditional out-of-distribution generation for unpaired data using transfer VAE M Lotfollahi, M Naghipourfar, FJ Theis, FA Wolf Bioinformatics 36 (Supplement_2), i610-i617, 2020 | 90* | 2020 |
| Machine learning for perturbational single-cell omics Y Ji, M Lotfollahi, FA Wolf, FJ Theis Cell Systems 12 (6), 522-537, 2021 | 74 | 2021 |
| 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 | 67 | 2023 |
| Biologically informed deep learning to query gene programs in single-cell atlases M Lotfollahi, S Rybakov, K Hrovatin, S Hediyeh-Zadeh, C Talavera-López, ... Nature Cell Biology 25 (2), 337-350, 2023 | 60* | 2023 |
| Multigrate: single-cell multi-omic data integration M Lotfollahi, A Litinetskaya, F Theis ICML 2021 Workshop on Computational Biology (WCB) Proceedings Paper, 2021 | 35 | 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 | 31 | 2024 |
| Predicting cellular responses to novel drug perturbations at a single-cell resolution L Hetzel, S Boehm, N Kilbertus, S Günnemann, M Lotfollahi, F Theis Advances in Neural Information Processing Systems 35, 26711-26722, 2022 | 28* | 2022 |
| Learning interpretable latent autoencoder representations with annotations of feature sets S Rybakov, M Lotfollahi, FJ Theis, FA Wolf MLCB 2020, 2020.12. 02.401182, 2020 | 28 | 2020 |
| Population-level integration of single-cell datasets enables multi-scale analysis across samples C De Donno, S Hediyeh-Zadeh, AA Moinfar, M Wagenstetter, L Zappia, ... Nature Methods, 1-10, 2023 | 21 | 2023 |
| Integrating T-cell receptor and transcriptome for large-scale single-cell immune profiling analysis F Drost, Y An, LM Dratva, RGH Lindeboom, M Haniffa, SA Teichmann, ... bioRxiv, 2021.06. 24.449733, 2021 | 16* | 2021 |
| Deep learning in spatially resolved transcriptfomics: a comprehensive technical view R Zahedi, R Ghamsari, A Argha, C Macphillamy, A Beheshti, ... Briefings in Bioinformatics 25 (2), bbae082, 2024 | 14* | 2024 |
| Single-cell reference mapping to construct and extend cell-type hierarchies L Michielsen, M Lotfollahi, D Strobl, L Sikkema, MJT Reinders, FJ Theis, ... NAR Genomics and Bioinformatics 5 (3), lqad070, 2023 | 11 | 2023 |
Fabian J TheisHelmholtz Munich, Technical University of Munich在 helmholtz-muenchen.de 的电子邮件经过验证
