Mo Lotfollahi
Mo Lotfollahi
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Cited by
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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
scGen predicts single-cell perturbation responses
M Lotfollahi, FA Wolf, FJ Theis
Nature methods 16 (8), 715-721, 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
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
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
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
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
Machine learning for perturbational single-cell omics
Y Ji, M Lotfollahi, FA Wolf, FJ Theis
Cell Systems 12 (6), 522-537, 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
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
Multigrate: single-cell multi-omic data integration
M Lotfollahi, A Litinetskaya, F Theis
ICML 2021 Workshop on Computational Biology (WCB) Proceedings Paper, 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
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
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
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
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
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
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
Modelling method using a conditional variational autoencoder
F Theis, M Lotfollahi, FA Wolf
US Patent App. 17/763,501, 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
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