| 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 | 607 | 2019 |
| scGen predicts single-cell perturbation responses M Lotfollahi, FA Wolf, FJ Theis Nature methods 16 (8), 715-721, 2019 | 178 | 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 | 141* | 2022 |
| 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 | 128 | 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 | 83 | 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 | 49* | 2020 |
| Learning interpretable cellular responses to complex perturbations in high-throughput screens M Lotfollahi, AK Susmelj, C De Donno, Y Ji, IL Ibarra, FA Wolf, ... bioRxiv, 2021 | 31* | 2021 |
| Machine learning for perturbational single-cell omics Y Ji, M Lotfollahi, FA Wolf, FJ Theis Cell Systems 12 (6), 522-537, 2021 | 27 | 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 | 20 | 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 | 18 | 2020 |
| Multigrate: single-cell multi-omic data integration M Lotfollahi, A Litinetskaya, F Theis ICML 2021 Workshop on Computational Biology (WCB) Proceedings Paper, 2021 | 11 | 2021 |
| Biologically informed deep learning to infer gene program activity in single cells M Lotfollahi, S Rybakov, K Hrovatin, S Hediyeh-zadeh, C Talavera-López, ... BioRxiv, 2022.02. 05.479217, 2022 | 5 | 2022 |
| Jointly learning T-cell receptor and transcriptomic information to decipher the immune response Y An, F Drost, F Theis, B Schubert, M Lotfollahi bioRxiv, 2021.06. 24.449733, 2021 | 3 | 2021 |
| 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 | 2 | 2022 |
| Predicting single-cell perturbation responses for unseen drugs L Hetzel, S Böhm, N Kilbertus, S Günnemann, M Lotfollahi, F Theis arXiv preprint arXiv:2204.13545, 2022 | 2 | 2022 |
| 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 |
| 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 | 2 | 2020 |
| Single-cell reference mapping to construct and extend cell type hierarchies L Michielsen, M Lotfollahi, D Strobl, L Sikkema, M Reinders, FJ Theis, ... bioRxiv, 2022.07. 07.499109, 2022 | 1 | 2022 |
| Modelling method using a conditional variational autoencoder F Theis, M Lotfollahi, FA Wolf US Patent App. 17/763,501, 2022 | | 2022 |
| Population-level integration of single-cell datasets enables multi-scale analysis across samples C De Donno, S Hediyeh-Zadeh, M Wagenstetter, AA Moinfar, L Zappia, ... bioRxiv, 2022.11. 28.517803, 2022 | | 2022 |
Fabian J TheisHelmholtz Munich, Technical University of MunichVerified email at helmholtz-muenchen.de
