Mo Lotfollahi
Mo Lotfollahi
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Cited by
Cited by
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
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
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
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
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
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
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
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
Machine learning for perturbational single-cell omics
Y Ji, M Lotfollahi, FA Wolf, FJ Theis
Cell Systems 12 (6), 522-537, 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
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
Multigrate: single-cell multi-omic data integration
M Lotfollahi, A Litinetskaya, F Theis
ICML 2021 Workshop on Computational Biology (WCB) Proceedings Paper, 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
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
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
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
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
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
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
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Articles 1–20