| 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 | 684 | 2019 |
| scGen predicts single-cell perturbation responses M Lotfollahi, FA Wolf, FJ Theis Nature methods 16 (8), 715-721, 2019 | 209 | 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 | 184 | 2022 |
| 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 | 181* | 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 | 110 | 2022 |
| 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 | 57* | 2020 |
| 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 | 41* | 2023 |
| Machine learning for perturbational single-cell omics Y Ji, M Lotfollahi, FA Wolf, FJ Theis Cell Systems 12 (6), 522-537, 2021 | 39 | 2021 |
| An integrated cell atlas of the human lung in health and disease L Sikkema, DC Strobl, L Zappia, E Madissoon, NS Markov, LE Zaragosi, ... bioRxiv, 2022.03. 10.483747, 2022 | 33 | 2022 |
| Learning interpretable latent autoencoder representations with annotations of feature sets S Rybakov, M Lotfollahi, FJ Theis, FA Wolf Machine Learning in Computational Biology (MLCB) meeting, 2020 | 23 | 2020 |
| Multigrate: single-cell multi-omic data integration M Lotfollahi, A Litinetskaya, F Theis ICML 2021 Workshop on Computational Biology (WCB) Proceedings Paper, 2021 | 17 | 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 | 14* | 2023 |
| 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 | 7* | 2022 |
| Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View RZ Nasab, MRE Ghamsari, A Argha, C Macphillamy, A Beheshti, ... arXiv preprint arXiv:2210.04453, 2022 | 7 | 2022 |
| 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 | 7* | 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, 1-3, 2023 | 5 | 2023 |
| Best practices for single-cell analysis across modalities L Heumos, AC Schaar, C Lance, A Litinetskaya, F Drost, L Zappia, ... Nature Reviews Genetics, 1-23, 2023 | 5 | 2023 |
| Single-cell reference mapping to construct and extend cell-type hierarchies L Michielsen, M Lotfollahi, D Strobl, L Sikkema, MJT Reinders, FJ Theis, ... bioRxiv, 2022.07. 07.499109, 2022 | 3 | 2022 |
| Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data M Lotfollahi, L Dony, H Agarwala, F Theis ICML 2020 Workshop on Computational Biology (WCB) Proceedings Paper 37, 2020 | 3 | 2020 |
| Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells A Gayoso, P Weiler, M Lotfollahi, D Klein, J Hong, AM Streets, FJ Theis, ... bioRxiv, 2022.08. 12.503709, 2022 | 2 | 2022 |
Fabian J TheisHelmholtz Munich, Technical University of MunichVerified email at helmholtz-muenchen.de
