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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發表在:	Molecular Systems Biology
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
格式:	Article
語言:	英语
出版:	Springer Nature 2023-05-01
主題:	generative modeling high‐throughput screening machine learning perturbation prediction single‐cell transcriptomics
在線閱讀:	https://doi.org/10.15252/msb.202211517