PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Ji, Y. ; Lotfollahi, M. ; Wolf, F.A. ; Theis, F.J.

Machine learning for perturbational single-cell omics.

Cell Syst. 12, 522-537 (2021)
Postprint DOI PMC
Open Access Green
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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
10.304
2.222
4
11
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Review
Keywords 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
Language english
Publication Year 2021
HGF-reported in Year 2021
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Journal Cell Systems
Quellenangaben Volume: 12, Issue: 6, Pages: 522-537 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Maryland Heights, MO
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants Helmholtz Association's Initiative and Networking Fund through sparse2big
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
BMBF
Silicon Valley Community Foundation
Chan Zuckerberg Initiative DAF
DOI 10.1016/j.cels.2021.05.016
WOS ID WOS:000663043500007
Scopus ID 85108163503
PubMed ID 34139164
Erfassungsdatum 2021-07-13
[➜Report correction]