Loading…

RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique

Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a re...

Full description

Saved in:
Bibliographic Details
Published in:BMC bioinformatics 2022-05, Vol.23 (1), p.165-165, Article 165
Main Authors: Jiang, Xiaohan, Zhang, Xiujun
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c512w-a6e561b2f6f71ab7dbba164e13a8b2c171f81d4af60d52fa9bdffc32ed1c6c513
cites cdi_FETCH-LOGICAL-c512w-a6e561b2f6f71ab7dbba164e13a8b2c171f81d4af60d52fa9bdffc32ed1c6c513
container_end_page 165
container_issue 1
container_start_page 165
container_title BMC bioinformatics
container_volume 23
creator Jiang, Xiaohan
Zhang, Xiujun
description Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.
doi_str_mv 10.1186/s12859-022-04696-w
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_24fa12a5ecc849d090eeedce4c302834</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A702801878</galeid><doaj_id>oai_doaj_org_article_24fa12a5ecc849d090eeedce4c302834</doaj_id><sourcerecordid>A702801878</sourcerecordid><originalsourceid>FETCH-LOGICAL-c512w-a6e561b2f6f71ab7dbba164e13a8b2c171f81d4af60d52fa9bdffc32ed1c6c513</originalsourceid><addsrcrecordid>eNptkkFv1DAQhSMEoqXwBzigSFzgkOJxHMfhgFRVBVaqQGrL2XLscTZL1m7thGX_PU63LV2EfLDl-d6zPX5Z9hrIMYDgHyJQUTUFobQgjDe82DzJDoHVUFAg1dNH64PsRYwrQqAWpHqeHZRVRRk05DDDi8tvZ1cf895ZDKF3Xd6hwzxgNw1q9GGbOxw3PvyMebvNVSqYyRnl9DaP_YBOzxLlzD2Wo1umKq7RjfmIeun6mwlfZs-sGiK-upuPsh-fz65Ovxbn378sTk_OC10B3RSKY8WhpZbbGlRbm7ZVwBlCqURLNdRgBRimLCemolY1rbFWlxQNaJ4syqNssfM1Xq3kdejXKmylV7283fChkyqMvR5QUmYVUFWh1oI1hjQEEY1GpktCRcmS16ed1_XUrueKG4Ma9kz3K65fys7_kg2pWUl5Mnh3ZxB86kEc5bqPGodBOfRTlJRzIIILThP69h905afgUqtmipeENVX1l-pUekD6MZ_O1bOpPKnTpQmIWiTq-D9UGgbXvfYObfq3fcH7PUFiRvw9dmqKUS4uL_ZZumN18DEGtA_9ACLnUMpdKGUKpbwNpdwk0ZvHnXyQ3Kew_AO5eN2y</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2666304955</pqid></control><display><type>article</type><title>RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique</title><source>PubMed (Medline)</source><source>Publicly Available Content Database</source><creator>Jiang, Xiaohan ; Zhang, Xiujun</creator><creatorcontrib>Jiang, Xiaohan ; Zhang, Xiujun</creatorcontrib><description>Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.</description><identifier>ISSN: 1471-2105</identifier><identifier>EISSN: 1471-2105</identifier><identifier>DOI: 10.1186/s12859-022-04696-w</identifier><identifier>PMID: 35524190</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Accuracy ; Algorithms ; Applications software ; Dependence ; E coli ; Experiments ; Gene expression ; Gene regulatory network ; Gene silencing ; Genetic engineering ; Genetic regulation ; Indirect interaction ; Linear programming ; Methods ; Network enhancement ; Network inference ; Networks ; Noise ; Optimization ; Optimization techniques ; Parameter estimation ; Performance evaluation ; Redundancy ; Redundancy silencing ; Ripening ; Simulation ; Software</subject><ispartof>BMC bioinformatics, 2022-05, Vol.23 (1), p.165-165, Article 165</ispartof><rights>2022. The Author(s).</rights><rights>COPYRIGHT 2022 BioMed Central Ltd.</rights><rights>2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c512w-a6e561b2f6f71ab7dbba164e13a8b2c171f81d4af60d52fa9bdffc32ed1c6c513</citedby><cites>FETCH-LOGICAL-c512w-a6e561b2f6f71ab7dbba164e13a8b2c171f81d4af60d52fa9bdffc32ed1c6c513</cites><orcidid>0000-0001-8041-0592</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9074326/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2666304955?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35524190$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jiang, Xiaohan</creatorcontrib><creatorcontrib>Zhang, Xiujun</creatorcontrib><title>RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique</title><title>BMC bioinformatics</title><addtitle>BMC Bioinformatics</addtitle><description>Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Applications software</subject><subject>Dependence</subject><subject>E coli</subject><subject>Experiments</subject><subject>Gene expression</subject><subject>Gene regulatory network</subject><subject>Gene silencing</subject><subject>Genetic engineering</subject><subject>Genetic regulation</subject><subject>Indirect interaction</subject><subject>Linear programming</subject><subject>Methods</subject><subject>Network enhancement</subject><subject>Network inference</subject><subject>Networks</subject><subject>Noise</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Parameter estimation</subject><subject>Performance evaluation</subject><subject>Redundancy</subject><subject>Redundancy silencing</subject><subject>Ripening</subject><subject>Simulation</subject><subject>Software</subject><issn>1471-2105</issn><issn>1471-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkkFv1DAQhSMEoqXwBzigSFzgkOJxHMfhgFRVBVaqQGrL2XLscTZL1m7thGX_PU63LV2EfLDl-d6zPX5Z9hrIMYDgHyJQUTUFobQgjDe82DzJDoHVUFAg1dNH64PsRYwrQqAWpHqeHZRVRRk05DDDi8tvZ1cf895ZDKF3Xd6hwzxgNw1q9GGbOxw3PvyMebvNVSqYyRnl9DaP_YBOzxLlzD2Wo1umKq7RjfmIeun6mwlfZs-sGiK-upuPsh-fz65Ovxbn378sTk_OC10B3RSKY8WhpZbbGlRbm7ZVwBlCqURLNdRgBRimLCemolY1rbFWlxQNaJ4syqNssfM1Xq3kdejXKmylV7283fChkyqMvR5QUmYVUFWh1oI1hjQEEY1GpktCRcmS16ed1_XUrueKG4Ma9kz3K65fys7_kg2pWUl5Mnh3ZxB86kEc5bqPGodBOfRTlJRzIIILThP69h905afgUqtmipeENVX1l-pUekD6MZ_O1bOpPKnTpQmIWiTq-D9UGgbXvfYObfq3fcH7PUFiRvw9dmqKUS4uL_ZZumN18DEGtA_9ACLnUMpdKGUKpbwNpdwk0ZvHnXyQ3Kew_AO5eN2y</recordid><startdate>20220506</startdate><enddate>20220506</enddate><creator>Jiang, Xiaohan</creator><creator>Zhang, Xiujun</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8041-0592</orcidid></search><sort><creationdate>20220506</creationdate><title>RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique</title><author>Jiang, Xiaohan ; Zhang, Xiujun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c512w-a6e561b2f6f71ab7dbba164e13a8b2c171f81d4af60d52fa9bdffc32ed1c6c513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Applications software</topic><topic>Dependence</topic><topic>E coli</topic><topic>Experiments</topic><topic>Gene expression</topic><topic>Gene regulatory network</topic><topic>Gene silencing</topic><topic>Genetic engineering</topic><topic>Genetic regulation</topic><topic>Indirect interaction</topic><topic>Linear programming</topic><topic>Methods</topic><topic>Network enhancement</topic><topic>Network inference</topic><topic>Networks</topic><topic>Noise</topic><topic>Optimization</topic><topic>Optimization techniques</topic><topic>Parameter estimation</topic><topic>Performance evaluation</topic><topic>Redundancy</topic><topic>Redundancy silencing</topic><topic>Ripening</topic><topic>Simulation</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Xiaohan</creatorcontrib><creatorcontrib>Zhang, Xiujun</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Biological Science Collection</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>BMC bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Xiaohan</au><au>Zhang, Xiujun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique</atitle><jtitle>BMC bioinformatics</jtitle><addtitle>BMC Bioinformatics</addtitle><date>2022-05-06</date><risdate>2022</risdate><volume>23</volume><issue>1</issue><spage>165</spage><epage>165</epage><pages>165-165</pages><artnum>165</artnum><issn>1471-2105</issn><eissn>1471-2105</eissn><abstract>Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>35524190</pmid><doi>10.1186/s12859-022-04696-w</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-8041-0592</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1471-2105
ispartof BMC bioinformatics, 2022-05, Vol.23 (1), p.165-165, Article 165
issn 1471-2105
1471-2105
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_24fa12a5ecc849d090eeedce4c302834
source PubMed (Medline); Publicly Available Content Database
subjects Accuracy
Algorithms
Applications software
Dependence
E coli
Experiments
Gene expression
Gene regulatory network
Gene silencing
Genetic engineering
Genetic regulation
Indirect interaction
Linear programming
Methods
Network enhancement
Network inference
Networks
Noise
Optimization
Optimization techniques
Parameter estimation
Performance evaluation
Redundancy
Redundancy silencing
Ripening
Simulation
Software
title RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T23%3A36%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=RSNET:%20inferring%20gene%20regulatory%20networks%20by%20a%20redundancy%20silencing%20and%20network%20enhancement%20technique&rft.jtitle=BMC%20bioinformatics&rft.au=Jiang,%20Xiaohan&rft.date=2022-05-06&rft.volume=23&rft.issue=1&rft.spage=165&rft.epage=165&rft.pages=165-165&rft.artnum=165&rft.issn=1471-2105&rft.eissn=1471-2105&rft_id=info:doi/10.1186/s12859-022-04696-w&rft_dat=%3Cgale_doaj_%3EA702801878%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c512w-a6e561b2f6f71ab7dbba164e13a8b2c171f81d4af60d52fa9bdffc32ed1c6c513%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2666304955&rft_id=info:pmid/35524190&rft_galeid=A702801878&rfr_iscdi=true