A review of systems biology research of anxiety disorders
Main Author: | |
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Publication Date: | 2021 |
Other Authors: | , , , , |
Format: | Article |
Language: | eng |
Source: | Brazilian Journal of Psychiatry (São Paulo. 1999. Online) |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462021000400414 |
Summary: | The development of “omic” technologies and deep phenotyping may facilitate a systems biology approach to understanding anxiety disorders. Systems biology approaches incorporate data from multiple modalities (e.g., genomic, neuroimaging) with functional analyses (e.g., animal and tissue culture models) and mathematical modeling (e.g., machine learning) to investigate pathological biophysical networks at various scales. Here we review: i) the neurobiology of anxiety disorders; ii) how systems biology approaches have advanced this work; and iii) the clinical implications and future directions of this research. Systems biology approaches have provided an improved functional understanding of candidate biomarkers and have suggested future potential for refining the diagnosis, prognosis, and treatment of anxiety disorders. The systems biology approach for anxiety disorders is, however, in its infancy and in some instances is characterized by insufficient power and replication. The studies reviewed here represent important steps to further untangling the pathophysiology of anxiety disorders. |
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A review of systems biology research of anxiety disordersAnxiety disorderssystems biologybiomarkersmachine learningThe development of “omic” technologies and deep phenotyping may facilitate a systems biology approach to understanding anxiety disorders. Systems biology approaches incorporate data from multiple modalities (e.g., genomic, neuroimaging) with functional analyses (e.g., animal and tissue culture models) and mathematical modeling (e.g., machine learning) to investigate pathological biophysical networks at various scales. Here we review: i) the neurobiology of anxiety disorders; ii) how systems biology approaches have advanced this work; and iii) the clinical implications and future directions of this research. Systems biology approaches have provided an improved functional understanding of candidate biomarkers and have suggested future potential for refining the diagnosis, prognosis, and treatment of anxiety disorders. The systems biology approach for anxiety disorders is, however, in its infancy and in some instances is characterized by insufficient power and replication. The studies reviewed here represent important steps to further untangling the pathophysiology of anxiety disorders.Associação Brasileira de Psiquiatria2021-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462021000400414Brazilian Journal of Psychiatry v.43 n.4 2021reponame:Brazilian Journal of Psychiatry (São Paulo. 1999. Online)instname:Associação Brasileira de Psiquiatria (ABP)instacron:ABP10.1590/1516-4446-2020-1090info:eu-repo/semantics/openAccessMufford,Mary S.van der Meer,DennisAndreassen,Ole A.Ramesar,RajStein,Dan J.Dalvie,Shareefaeng2021-08-23T00:00:00Zoai:scielo:S1516-44462021000400414Revistahttp://www.bjp.org.br/ahead_of_print.asphttps://old.scielo.br/oai/scielo-oai.php||rbp@abpbrasil.org.br1809-452X1516-4446opendoar:2021-08-23T00:00Brazilian Journal of Psychiatry (São Paulo. 1999. Online) - Associação Brasileira de Psiquiatria (ABP)false |
dc.title.none.fl_str_mv |
A review of systems biology research of anxiety disorders |
title |
A review of systems biology research of anxiety disorders |
spellingShingle |
A review of systems biology research of anxiety disorders Mufford,Mary S. Anxiety disorders systems biology biomarkers machine learning |
title_short |
A review of systems biology research of anxiety disorders |
title_full |
A review of systems biology research of anxiety disorders |
title_fullStr |
A review of systems biology research of anxiety disorders |
title_full_unstemmed |
A review of systems biology research of anxiety disorders |
title_sort |
A review of systems biology research of anxiety disorders |
author |
Mufford,Mary S. |
author_facet |
Mufford,Mary S. van der Meer,Dennis Andreassen,Ole A. Ramesar,Raj Stein,Dan J. Dalvie,Shareefa |
author_role |
author |
author2 |
van der Meer,Dennis Andreassen,Ole A. Ramesar,Raj Stein,Dan J. Dalvie,Shareefa |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Mufford,Mary S. van der Meer,Dennis Andreassen,Ole A. Ramesar,Raj Stein,Dan J. Dalvie,Shareefa |
dc.subject.por.fl_str_mv |
Anxiety disorders systems biology biomarkers machine learning |
topic |
Anxiety disorders systems biology biomarkers machine learning |
description |
The development of “omic” technologies and deep phenotyping may facilitate a systems biology approach to understanding anxiety disorders. Systems biology approaches incorporate data from multiple modalities (e.g., genomic, neuroimaging) with functional analyses (e.g., animal and tissue culture models) and mathematical modeling (e.g., machine learning) to investigate pathological biophysical networks at various scales. Here we review: i) the neurobiology of anxiety disorders; ii) how systems biology approaches have advanced this work; and iii) the clinical implications and future directions of this research. Systems biology approaches have provided an improved functional understanding of candidate biomarkers and have suggested future potential for refining the diagnosis, prognosis, and treatment of anxiety disorders. The systems biology approach for anxiety disorders is, however, in its infancy and in some instances is characterized by insufficient power and replication. The studies reviewed here represent important steps to further untangling the pathophysiology of anxiety disorders. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-08-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462021000400414 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462021000400414 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1516-4446-2020-1090 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Psiquiatria |
publisher.none.fl_str_mv |
Associação Brasileira de Psiquiatria |
dc.source.none.fl_str_mv |
Brazilian Journal of Psychiatry v.43 n.4 2021 reponame:Brazilian Journal of Psychiatry (São Paulo. 1999. Online) instname:Associação Brasileira de Psiquiatria (ABP) instacron:ABP |
instname_str |
Associação Brasileira de Psiquiatria (ABP) |
instacron_str |
ABP |
institution |
ABP |
reponame_str |
Brazilian Journal of Psychiatry (São Paulo. 1999. Online) |
collection |
Brazilian Journal of Psychiatry (São Paulo. 1999. Online) |
repository.name.fl_str_mv |
Brazilian Journal of Psychiatry (São Paulo. 1999. Online) - Associação Brasileira de Psiquiatria (ABP) |
repository.mail.fl_str_mv |
||rbp@abpbrasil.org.br |
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1754212560434888704 |