| 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 | 1094 | 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 | 618 | 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 | 564 | 2023 |
| 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 | 445 | 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 | 440 | 2022 |
| scGen predicts single-cell perturbation responses M Lotfollahi, FA Wolf, FJ Theis Nature methods 16 (8), 715-721, 2019 | 439 | 2019 |
| 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 | 427* | 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 | 183* | 2023 |
| 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 | 145 | 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 | 121* | 2020 |
| Machine learning for perturbational single-cell omics Y Ji, M Lotfollahi, FA Wolf, FJ Theis Cell Systems 12 (6), 522-537, 2021 | 105 | 2021 |
| 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 | 83* | 2023 |
| 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 | 63 | 2024 |
| 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 | 59 | 2023 |
| Predicting cellular responses to novel drug perturbations at a single-cell resolution L Hetzel, S Boehm, N Kilbertus, S Günnemann, F Theis Advances in Neural Information Processing Systems 35, 26711-26722, 2022 | 54 | 2022 |
| Multigrate: single-cell multi-omic data integration M Lotfollahi, A Litinetskaya, F Theis ICML 2021 Workshop on Computational Biology (WCB) Proceedings Paper, 2021 | 47 | 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 | 30* | 2024 |
| 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 | 30 | 2020 |
| The future of rapid and automated single-cell data analysis using reference mapping M Lotfollahi, Y Hao, FJ Theis, R Satija Cell 187 (10), 2343-2358, 2024 | 24 | 2024 |
| An integrated single-cell reference atlas of the human endometrium M Marečková, L Garcia-Alonso, M Moullet, V Lorenzi, R Petryszak, ... Nature Genetics 56 (9), 1925-1937, 2024 | 23 | 2024 |
Fabian J TheisHelmholtz Munich, Technical University of MunichΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα helmholtz-muenchen.de