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Network approaches for omics studies of neurodegenerative diseases

The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states...

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Autores principales: Zhao, Na, Quicksall, Zachary, Asmann, Yan W., Ren, Yingxue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523597/
https://www.ncbi.nlm.nih.gov/pubmed/36186441
http://dx.doi.org/10.3389/fgene.2022.984338
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author Zhao, Na
Quicksall, Zachary
Asmann, Yan W.
Ren, Yingxue
author_facet Zhao, Na
Quicksall, Zachary
Asmann, Yan W.
Ren, Yingxue
author_sort Zhao, Na
collection PubMed
description The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.
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spelling pubmed-95235972022-10-01 Network approaches for omics studies of neurodegenerative diseases Zhao, Na Quicksall, Zachary Asmann, Yan W. Ren, Yingxue Front Genet Genetics The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases. Frontiers Media S.A. 2022-09-16 /pmc/articles/PMC9523597/ /pubmed/36186441 http://dx.doi.org/10.3389/fgene.2022.984338 Text en Copyright © 2022 Zhao, Quicksall, Asmann and Ren. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Zhao, Na
Quicksall, Zachary
Asmann, Yan W.
Ren, Yingxue
Network approaches for omics studies of neurodegenerative diseases
title Network approaches for omics studies of neurodegenerative diseases
title_full Network approaches for omics studies of neurodegenerative diseases
title_fullStr Network approaches for omics studies of neurodegenerative diseases
title_full_unstemmed Network approaches for omics studies of neurodegenerative diseases
title_short Network approaches for omics studies of neurodegenerative diseases
title_sort network approaches for omics studies of neurodegenerative diseases
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523597/
https://www.ncbi.nlm.nih.gov/pubmed/36186441
http://dx.doi.org/10.3389/fgene.2022.984338
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