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Mohammad Lotfollahi

Showing results (1-10 of 25) with videos related to

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Nature Methods|August 9, 2024
Toward learning a foundational representation of cells and genesMohammad Lotfollahi
Nature Communications|January 8, 2025
Predicting cell morphological responses to perturbations using generative modelingAlessandro Palma, Fabian J Theis, Mohammad Lotfollahi
Nature Methods|August 1, 2019
scGen predicts single-cell perturbation responsesMohammad Lotfollahi, F Alexander Wolf, Fabian J Theis
Cell|May 10, 2024
The future of rapid and automated single-cell data analysis using reference mappingMohammad Lotfollahi, Yuhan Hao, Fabian J Theis, et al.
Cell Systems|June 17, 2021
Machine learning for perturbational single-cell omicsYuge Ji, Mohammad Lotfollahi, F Alexander Wolf, et al.
Bioinformatics (Oxford, England)|December 31, 2020
Conditional out-of-distribution generation for unpaired data using transfer VAEMohammad Lotfollahi, Mohsen Naghipourfar, Fabian J Theis, et al.
Nature Methods|September 22, 2023
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cellsAdam Gayoso, Philipp Weiler, Mohammad Lotfollahi, et al.
NAR Genomics and Bioinformatics|July 28, 2023
Single-cell reference mapping to construct and extend cell-type hierarchiesLieke Michielsen, Mohammad Lotfollahi, Daniel Strobl, et al.
Nature Methods|October 9, 2023
Population-level integration of single-cell datasets enables multi-scale analysis across samplesCarlo De Donno, Soroor Hediyeh-Zadeh, Amir Ali Moinfar, et al.
Nature Cell Biology|February 2, 2023
Biologically informed deep learning to query gene programs in single-cell atlasesMohammad Lotfollahi, Sergei Rybakov, Karin Hrovatin, et al.
Pageof 3

Showing results (1-10 of 25) with videos related to

Sort By:
Pageof 3
Nature Methods|August 9, 2024
Toward learning a foundational representation of cells and genesMohammad Lotfollahi
Nature Communications|January 8, 2025
Predicting cell morphological responses to perturbations using generative modelingAlessandro Palma, Fabian J Theis, Mohammad Lotfollahi
Nature Methods|August 1, 2019
scGen predicts single-cell perturbation responsesMohammad Lotfollahi, F Alexander Wolf, Fabian J Theis
Cell|May 10, 2024
The future of rapid and automated single-cell data analysis using reference mappingMohammad Lotfollahi, Yuhan Hao, Fabian J Theis, et al.
Cell Systems|June 17, 2021
Machine learning for perturbational single-cell omicsYuge Ji, Mohammad Lotfollahi, F Alexander Wolf, et al.
Bioinformatics (Oxford, England)|December 31, 2020
Conditional out-of-distribution generation for unpaired data using transfer VAEMohammad Lotfollahi, Mohsen Naghipourfar, Fabian J Theis, et al.
Nature Methods|September 22, 2023
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cellsAdam Gayoso, Philipp Weiler, Mohammad Lotfollahi, et al.
NAR Genomics and Bioinformatics|July 28, 2023
Single-cell reference mapping to construct and extend cell-type hierarchiesLieke Michielsen, Mohammad Lotfollahi, Daniel Strobl, et al.
Nature Methods|October 9, 2023
Population-level integration of single-cell datasets enables multi-scale analysis across samplesCarlo De Donno, Soroor Hediyeh-Zadeh, Amir Ali Moinfar, et al.
Nature Cell Biology|February 2, 2023
Biologically informed deep learning to query gene programs in single-cell atlasesMohammad Lotfollahi, Sergei Rybakov, Karin Hrovatin, et al.
Pageof 3