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Network modeling of single-cell omics data: challenges, opportunities, and progresses

Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processe...

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Detalles Bibliográficos
Autores principales: Blencowe, Montgomery, Arneson, Douglas, Ding, Jessica, Chen, Yen-Wei, Saleem, Zara, Yang, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Portland Press Ltd. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141415/
https://www.ncbi.nlm.nih.gov/pubmed/32270049
http://dx.doi.org/10.1042/ETLS20180176
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author Blencowe, Montgomery
Arneson, Douglas
Ding, Jessica
Chen, Yen-Wei
Saleem, Zara
Yang, Xia
author_facet Blencowe, Montgomery
Arneson, Douglas
Ding, Jessica
Chen, Yen-Wei
Saleem, Zara
Yang, Xia
author_sort Blencowe, Montgomery
collection PubMed
description Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell–cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.
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spelling pubmed-71414152020-04-08 Network modeling of single-cell omics data: challenges, opportunities, and progresses Blencowe, Montgomery Arneson, Douglas Ding, Jessica Chen, Yen-Wei Saleem, Zara Yang, Xia Emerg Top Life Sci Review Articles Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell–cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward. Portland Press Ltd. 2019-08-16 2019-07-08 /pmc/articles/PMC7141415/ /pubmed/32270049 http://dx.doi.org/10.1042/ETLS20180176 Text en © 2019 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Review Articles
Blencowe, Montgomery
Arneson, Douglas
Ding, Jessica
Chen, Yen-Wei
Saleem, Zara
Yang, Xia
Network modeling of single-cell omics data: challenges, opportunities, and progresses
title Network modeling of single-cell omics data: challenges, opportunities, and progresses
title_full Network modeling of single-cell omics data: challenges, opportunities, and progresses
title_fullStr Network modeling of single-cell omics data: challenges, opportunities, and progresses
title_full_unstemmed Network modeling of single-cell omics data: challenges, opportunities, and progresses
title_short Network modeling of single-cell omics data: challenges, opportunities, and progresses
title_sort network modeling of single-cell omics data: challenges, opportunities, and progresses
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141415/
https://www.ncbi.nlm.nih.gov/pubmed/32270049
http://dx.doi.org/10.1042/ETLS20180176
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