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Machine learning for perturbational single-cell omics.

Cell Syst. 12, 522-537 (2021)
Verlagsversion DOI
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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Cell State ; Deep Learning ; Drug ; Heterogeneous Systems ; Machine Learning ; Perturbation ; Single-cell; Rna-seq Data; Drug-sensitivity; Model; Prediction; Target; Identification; Heterogeneity; Combinations; Phenotypes; Signatures
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Zeitschrift Cell Systems
Quellenangaben Band: 12, Heft: 6, Seiten: 522-537 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Maryland Heights, MO
Förderungen Helmholtz Association's Initiative and Networking Fund through sparse2big
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
Silicon Valley Community Foundation
Chan Zuckerberg Initiative DAF