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Mohammad Lotfollahi

Showing results (11-20 of 25) with videos related to

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Briefings in Bioinformatics|March 14, 2024
Deep learning in spatially resolved transcriptfomics: a comprehensive technical viewRoxana Zahedi, Reza Ghamsari, Ahmadreza Argha, et al.
Nature|April 16, 2025
Towards multimodal foundation models in molecular cell biologyHaotian Cui, Alejandro Tejada-Lapuerta, Maria Brbić, et al.
Nature Communications|July 2, 2024
Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression dataFelix Drost, Yang An, Irene Bonafonte-Pardàs, et al.
Nature Methods|February 1, 2022
Squidpy: a scalable framework for spatial omics analysisGiovanni Palla, Hannah Spitzer, Michal Klein, et al.
Nature Biotechnology|August 31, 2021
Mapping single-cell data to reference atlases by transfer learningMohammad Lotfollahi, Mohsen Naghipourfar, Malte D Luecken, et al.
Heliyon|May 5, 2023
Single-cell RNA sequencing uncovers heterogeneous transcriptional signatures in tumor-infiltrated dendritic cells in prostate cancerAdib Miraki Feriz, Arezou Khosrojerdi, Mohammad Lotfollahi, et al.
Nature Genetics|March 19, 2025
Quantitative characterization of cell niches in spatially resolved omics dataSebastian Birk, Irene Bonafonte-Pardàs, Adib Miraki Feriz, et al.
Molecular Systems Biology|May 8, 2023
Predicting cellular responses to complex perturbations in high-throughput screensMohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, et al.
Science China. Life Sciences|March 18, 2025
Biomedical data and AIHao Xu, Shibo Zhou, Zefeng Zhu, et al.
Nature Methods|December 31, 2025
Pertpy: an end-to-end framework for perturbation analysisLukas Heumos, Yuge Ji, Lilly May, et al.
Pageof 3

Showing results (11-20 of 25) with videos related to

Sort By:
Pageof 3
Briefings in Bioinformatics|March 14, 2024
Deep learning in spatially resolved transcriptfomics: a comprehensive technical viewRoxana Zahedi, Reza Ghamsari, Ahmadreza Argha, et al.
Nature|April 16, 2025
Towards multimodal foundation models in molecular cell biologyHaotian Cui, Alejandro Tejada-Lapuerta, Maria Brbić, et al.
Nature Communications|July 2, 2024
Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression dataFelix Drost, Yang An, Irene Bonafonte-Pardàs, et al.
Nature Methods|February 1, 2022
Squidpy: a scalable framework for spatial omics analysisGiovanni Palla, Hannah Spitzer, Michal Klein, et al.
Nature Biotechnology|August 31, 2021
Mapping single-cell data to reference atlases by transfer learningMohammad Lotfollahi, Mohsen Naghipourfar, Malte D Luecken, et al.
Heliyon|May 5, 2023
Single-cell RNA sequencing uncovers heterogeneous transcriptional signatures in tumor-infiltrated dendritic cells in prostate cancerAdib Miraki Feriz, Arezou Khosrojerdi, Mohammad Lotfollahi, et al.
Nature Genetics|March 19, 2025
Quantitative characterization of cell niches in spatially resolved omics dataSebastian Birk, Irene Bonafonte-Pardàs, Adib Miraki Feriz, et al.
Molecular Systems Biology|May 8, 2023
Predicting cellular responses to complex perturbations in high-throughput screensMohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, et al.
Science China. Life Sciences|March 18, 2025
Biomedical data and AIHao Xu, Shibo Zhou, Zefeng Zhu, et al.
Nature Methods|December 31, 2025
Pertpy: an end-to-end framework for perturbation analysisLukas Heumos, Yuge Ji, Lilly May, et al.
Pageof 3