Ji Y, Lotfollahi M, Wolf FA, Theis FJ (2021)
Publication Type: Journal article, Review article
Publication year: 2021
Book Volume: 12
Pages Range: 522-537
Journal Issue: 6
DOI: 10.1016/j.cels.2021.05.016
Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
APA:
Ji, Y., Lotfollahi, M., Wolf, F.A., & Theis, F.J. (2021). Machine learning for perturbational single-cell omics. Cell Systems, 12(6), 522-537. https://doi.org/10.1016/j.cels.2021.05.016
MLA:
Ji, Yuge, et al. "Machine learning for perturbational single-cell omics." Cell Systems 12.6 (2021): 522-537.
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