A comprehensive overview and critical evaluation of gene regulatory network inference technologies

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Briefings in bioinformatics 2021-09, Vol.22 (5)
Hauptverfasser: Zhao, Mengyuan, He, Wenying, Tang, Jijun, Zou, Quan, Guo, Fei
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 5
container_start_page
container_title Briefings in bioinformatics
container_volume 22
creator Zhao, Mengyuan
He, Wenying
Tang, Jijun
Zou, Quan
Guo, Fei
description Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.
doi_str_mv 10.1093/bib/bbab009
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2487153791</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2487153791</sourcerecordid><originalsourceid>FETCH-LOGICAL-c317t-a706e19d42a159efae302ecab9609fbeda547edff2436d434a90d047fd42b2143</originalsourceid><addsrcrecordid>eNo9kD1PwzAYhC0EoqUwsSOPSCjUjp24HquKL6kSC8yR7bxuDald7CRV_z2pWpjuhudueBC6peSREsmm2ump1koTIs_QmHIhMk4Kfn7opcgKXrIRukrpi5CciBm9RCPGCiYLysdIz7EJm22ENfjkesChh9g72GHla2yia51RDYZeNZ1qXfA4WLwCDzjCqmtUG-Iee2h3IX5j5y1E8AZwC2btQxNWDtI1urCqSXBzygn6fH76WLxmy_eXt8V8mRlGRZspQUqgsua5ooUEq4CRHIzSsiTSaqhVwQXU1uaclTVnXElSEy7ssNA55WyC7o-_2xh-OkhttXHJQNMoD6FLVc5nghZMSDqgD0fUxJBSBFtto9uouK8oqQ5Sq0FqdZI60Hen405voP5n_yyyX1-9dfs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2487153791</pqid></control><display><type>article</type><title>A comprehensive overview and critical evaluation of gene regulatory network inference technologies</title><source>Access via Oxford University Press (Open Access Collection)</source><source>EBSCOhost Business Source Complete</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Zhao, Mengyuan ; He, Wenying ; Tang, Jijun ; Zou, Quan ; Guo, Fei</creator><creatorcontrib>Zhao, Mengyuan ; He, Wenying ; Tang, Jijun ; Zou, Quan ; Guo, Fei</creatorcontrib><description>Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.</description><identifier>ISSN: 1467-5463</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbab009</identifier><identifier>PMID: 33539514</identifier><language>eng</language><publisher>England</publisher><ispartof>Briefings in bioinformatics, 2021-09, Vol.22 (5)</ispartof><rights>The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c317t-a706e19d42a159efae302ecab9609fbeda547edff2436d434a90d047fd42b2143</citedby><cites>FETCH-LOGICAL-c317t-a706e19d42a159efae302ecab9609fbeda547edff2436d434a90d047fd42b2143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33539514$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Mengyuan</creatorcontrib><creatorcontrib>He, Wenying</creatorcontrib><creatorcontrib>Tang, Jijun</creatorcontrib><creatorcontrib>Zou, Quan</creatorcontrib><creatorcontrib>Guo, Fei</creatorcontrib><title>A comprehensive overview and critical evaluation of gene regulatory network inference technologies</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.</description><issn>1467-5463</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNo9kD1PwzAYhC0EoqUwsSOPSCjUjp24HquKL6kSC8yR7bxuDald7CRV_z2pWpjuhudueBC6peSREsmm2ump1koTIs_QmHIhMk4Kfn7opcgKXrIRukrpi5CciBm9RCPGCiYLysdIz7EJm22ENfjkesChh9g72GHla2yia51RDYZeNZ1qXfA4WLwCDzjCqmtUG-Iee2h3IX5j5y1E8AZwC2btQxNWDtI1urCqSXBzygn6fH76WLxmy_eXt8V8mRlGRZspQUqgsua5ooUEq4CRHIzSsiTSaqhVwQXU1uaclTVnXElSEy7ssNA55WyC7o-_2xh-OkhttXHJQNMoD6FLVc5nghZMSDqgD0fUxJBSBFtto9uouK8oqQ5Sq0FqdZI60Hen405voP5n_yyyX1-9dfs</recordid><startdate>20210902</startdate><enddate>20210902</enddate><creator>Zhao, Mengyuan</creator><creator>He, Wenying</creator><creator>Tang, Jijun</creator><creator>Zou, Quan</creator><creator>Guo, Fei</creator><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20210902</creationdate><title>A comprehensive overview and critical evaluation of gene regulatory network inference technologies</title><author>Zhao, Mengyuan ; He, Wenying ; Tang, Jijun ; Zou, Quan ; Guo, Fei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c317t-a706e19d42a159efae302ecab9609fbeda547edff2436d434a90d047fd42b2143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Mengyuan</creatorcontrib><creatorcontrib>He, Wenying</creatorcontrib><creatorcontrib>Tang, Jijun</creatorcontrib><creatorcontrib>Zou, Quan</creatorcontrib><creatorcontrib>Guo, Fei</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Mengyuan</au><au>He, Wenying</au><au>Tang, Jijun</au><au>Zou, Quan</au><au>Guo, Fei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comprehensive overview and critical evaluation of gene regulatory network inference technologies</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2021-09-02</date><risdate>2021</risdate><volume>22</volume><issue>5</issue><issn>1467-5463</issn><eissn>1477-4054</eissn><abstract>Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.</abstract><cop>England</cop><pmid>33539514</pmid><doi>10.1093/bib/bbab009</doi></addata></record>
fulltext fulltext
identifier ISSN: 1467-5463
ispartof Briefings in bioinformatics, 2021-09, Vol.22 (5)
issn 1467-5463
1477-4054
language eng
recordid cdi_proquest_miscellaneous_2487153791
source Access via Oxford University Press (Open Access Collection); EBSCOhost Business Source Complete; EZB-FREE-00999 freely available EZB journals; PubMed Central
title A comprehensive overview and critical evaluation of gene regulatory network inference technologies
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T13%3A23%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20comprehensive%20overview%20and%20critical%20evaluation%20of%20gene%20regulatory%20network%20inference%20technologies&rft.jtitle=Briefings%20in%20bioinformatics&rft.au=Zhao,%20Mengyuan&rft.date=2021-09-02&rft.volume=22&rft.issue=5&rft.issn=1467-5463&rft.eissn=1477-4054&rft_id=info:doi/10.1093/bib/bbab009&rft_dat=%3Cproquest_cross%3E2487153791%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2487153791&rft_id=info:pmid/33539514&rfr_iscdi=true