Predicting cellular responses to complex perturbations in high‐throughput screens

Predicting cellular responses to complex perturbations in high‐throughput screens

Abstract Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed...

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Main Authors: Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio L Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, Jay Shendure, Jose L McFaline‐Figueroa, Pierre Boyeau, F Alexander Wolf, Nafissa Yakubova, Stephan Günnemann, Cole Trapnell, David Lopez‐Paz, Fabian J Theis
Format: Article
Language:English
Published: Springer Nature 2023-05-01
Series:Molecular Systems Biology
Subjects:
generative modeling
high‐throughput screening
machine learning
perturbation prediction
single‐cell transcriptomics
Online Access:https://doi.org/10.15252/msb.202211517
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https://doi.org/10.15252/msb.202211517

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