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Graph representation learning for single cell biology.
Curr. Opin. Syst. Biol. 28:100347 (2021)
Single cell RNA sequencing measures gene expression at an unprecedented resolution and scale and allows the analysis of cellular phenotypes which was not possible before. In this context, graphs occur as a natural representation of the system - both as gene-centric and cell-centric. However, many advances in machine learning on graphs are not yet harnessed in models on single-cell data. Taking the inference of cell types or gene interactions as examples, graph representation learning has a wide applicability to both cell and gene graphs. Recent advances in spatial molecular profiling additionally put graph-learning in the focus of attention due the innate resemblance of spatial information to spatial graphs. We argue that graph embedding techniques have great potential for various applications across single cell biology. Here, we discuss how graph representation learning maps to current models and concepts used in single cell biology and formalise overlaps to developments in graph-based deep learning.
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Publication type
Article: Journal article
Document type
Review
Language
english
Publication Year
2021
HGF-reported in Year
2022
ISSN (print) / ISBN
2452-3100
e-ISSN
2452-3100
Quellenangaben
Volume: 28,
Article Number: 100347
Publisher
Elsevier
Publishing Place
Amsterdam
Reviewing status
Peer reviewed
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
Bundesministerium für Bildung und Forschung
Helmholtz Association's Initiative and Networking Fund
Helmholtz AI
Helmholtz Association's Initiative and Networking Fund
Helmholtz AI
Erfassungsdatum
2022-08-31