Author:
Wang Haixin,Wang Yunhan,Jiang Qun,Zhang Yan,Chen Shengquan
Publisher
Springer Science and Business Media LLC
Reference12 articles.
1. Lotfollahi M, Wolf F A, Theis F J. scGen predicts single-cell perturbation responses. Nature Methods, 2019, 16(8): 715–721
2. Ji Y, Lotfollahi M, Wolf F A, Theis F J. Machine learning for perturbational single-cell omics. Cell Systems, 2021, 12(6): 522–537
3. Wei X, Dong J, Wang F. scPreGAN, a deep generative model for predicting the response of single-cell expression to perturbation. Bioinformatics, 2022, 38(13): 3377–3384
4. He K, Chen X, Xie S, Li Y, Dollár P, Girshick R. Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, 15979–15988
5. Makkuva A V, Taghvaei A, Lee J D, Oh S. Optimal transport mapping via input convex neural networks. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 619