Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information.
Identifieur interne : 000749 ( PubMed/Curation ); précédent : 000748; suivant : 000750Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information.
Auteurs : Zhao-Hui Zhan [République populaire de Chine] ; Zhu-Hong You [République populaire de Chine] ; Li-Ping Li [République populaire de Chine] ; Yong Zhou [République populaire de Chine] ; Hai-Cheng Yi [République populaire de Chine]Source :
- Frontiers in genetics [ 1664-8021 ] ; 2018.
Abstract
Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.
DOI: 10.3389/fgene.2018.00458
PubMed: 30349558
Links toward previous steps (curation, corpus...)
- to stream PubMed, to step Corpus: Pour aller vers cette notice dans l'étape Curation :000749
Links to Exploration step
pubmed:30349558Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information.</title>
<author><name sortKey="Zhan, Zhao Hui" sort="Zhan, Zhao Hui" uniqKey="Zhan Z" first="Zhao-Hui" last="Zhan">Zhao-Hui Zhan</name>
<affiliation wicri:level="1"><nlm:affiliation>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="You, Zhu Hong" sort="You, Zhu Hong" uniqKey="You Z" first="Zhu-Hong" last="You">Zhu-Hong You</name>
<affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Li, Li Ping" sort="Li, Li Ping" uniqKey="Li L" first="Li-Ping" last="Li">Li-Ping Li</name>
<affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name>
<affiliation wicri:level="1"><nlm:affiliation>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Yi, Hai Cheng" sort="Yi, Hai Cheng" uniqKey="Yi H" first="Hai-Cheng" last="Yi">Hai-Cheng Yi</name>
<affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi</wicri:regionArea>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2018">2018</date>
<idno type="RBID">pubmed:30349558</idno>
<idno type="pmid">30349558</idno>
<idno type="doi">10.3389/fgene.2018.00458</idno>
<idno type="wicri:Area/PubMed/Corpus">000749</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000749</idno>
<idno type="wicri:Area/PubMed/Curation">000749</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000749</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information.</title>
<author><name sortKey="Zhan, Zhao Hui" sort="Zhan, Zhao Hui" uniqKey="Zhan Z" first="Zhao-Hui" last="Zhan">Zhao-Hui Zhan</name>
<affiliation wicri:level="1"><nlm:affiliation>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="You, Zhu Hong" sort="You, Zhu Hong" uniqKey="You Z" first="Zhu-Hong" last="You">Zhu-Hong You</name>
<affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Li, Li Ping" sort="Li, Li Ping" uniqKey="Li L" first="Li-Ping" last="Li">Li-Ping Li</name>
<affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name>
<affiliation wicri:level="1"><nlm:affiliation>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou</wicri:regionArea>
</affiliation>
</author>
<author><name sortKey="Yi, Hai Cheng" sort="Yi, Hai Cheng" uniqKey="Yi H" first="Hai-Cheng" last="Yi">Hai-Cheng Yi</name>
<affiliation wicri:level="1"><nlm:affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi</wicri:regionArea>
</affiliation>
</author>
</analytic>
<series><title level="j">Frontiers in genetics</title>
<idno type="ISSN">1664-8021</idno>
<imprint><date when="2018" type="published">2018</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">30349558</PMID>
<DateRevised><Year>2019</Year>
<Month>11</Month>
<Day>20</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1664-8021</ISSN>
<JournalIssue CitedMedium="Print"><Volume>9</Volume>
<PubDate><Year>2018</Year>
</PubDate>
</JournalIssue>
<Title>Frontiers in genetics</Title>
<ISOAbbreviation>Front Genet</ISOAbbreviation>
</Journal>
<ArticleTitle>Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information.</ArticleTitle>
<Pagination><MedlinePgn>458</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.3389/fgene.2018.00458</ELocationID>
<Abstract><AbstractText>Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhan</LastName>
<ForeName>Zhao-Hui</ForeName>
<Initials>ZH</Initials>
<AffiliationInfo><Affiliation>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>You</LastName>
<ForeName>Zhu-Hong</ForeName>
<Initials>ZH</Initials>
<AffiliationInfo><Affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Li-Ping</ForeName>
<Initials>LP</Initials>
<AffiliationInfo><Affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhou</LastName>
<ForeName>Yong</ForeName>
<Initials>Y</Initials>
<AffiliationInfo><Affiliation>School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Yi</LastName>
<ForeName>Hai-Cheng</ForeName>
<Initials>HC</Initials>
<AffiliationInfo><Affiliation>Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2018</Year>
<Month>10</Month>
<Day>08</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>Switzerland</Country>
<MedlineTA>Front Genet</MedlineTA>
<NlmUniqueID>101560621</NlmUniqueID>
<ISSNLinking>1664-8021</ISSNLinking>
</MedlineJournalInfo>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">LightGBM</Keyword>
<Keyword MajorTopicYN="N">PSSM</Keyword>
<Keyword MajorTopicYN="N">Pseudo-Zernike moments</Keyword>
<Keyword MajorTopicYN="N">k-mers</Keyword>
<Keyword MajorTopicYN="N">ncRNA-protein interactions</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year>
<Month>08</Month>
<Day>07</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2018</Year>
<Month>09</Month>
<Day>19</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2018</Year>
<Month>10</Month>
<Day>24</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2018</Year>
<Month>10</Month>
<Day>24</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2018</Year>
<Month>10</Month>
<Day>24</Day>
<Hour>6</Hour>
<Minute>1</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">30349558</ArticleId>
<ArticleId IdType="doi">10.3389/fgene.2018.00458</ArticleId>
<ArticleId IdType="pmc">PMC6186793</ArticleId>
</ArticleIdList>
<ReferenceList><Reference><Citation>Int J Mol Sci. 2017 May 11;18(5):null</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28492483</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Mol Ther Nucleic Acids. 2018 Jun 1;11:337-344</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29858068</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Syst Biol. 2017 Mar 14;11(Suppl 2):9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28361676</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Protein Cell. 2012 Jul;3(7):508-20</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22729399</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Sci Rep. 2017 Apr 4;7(1):614</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28377624</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Biomed Res Int. 2015;2015:902198</Citation>
<ArticleIdList><ArticleId IdType="pubmed">26634213</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Bioinformatics. 2011 Dec 22;12:489</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22192482</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Genomics. 2018 Jul 3;19(1):511</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29970003</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Methods. 2017 Aug 15;126:18-28</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28651966</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nucleic Acids Res. 2015 Feb 18;43(3):1370-9</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25609700</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Mol Biosyst. 2013 Jan 27;9(1):133-42</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23138266</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>RNA Biol. 2011 Mar-Apr;8(2):305-15</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21505273</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Cancer Lett. 2018 Apr 10;419:53-63</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29371021</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Bioinformatics. 2010 Mar 1;26(5):680-2</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20053844</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Genomics. 2013 Sep 24;14:651</Citation>
<ArticleIdList><ArticleId IdType="pubmed">24063787</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Theor Biol. 2013 Mar 7;320:41-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23246717</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Theor Biol. 2011 Feb 21;271(1):10-7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21110985</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Cybern. 2017 Mar;47(3):731-743</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28113829</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Theor Biol. 2016 Aug 7;402:1-8</Citation>
<ArticleIdList><ArticleId IdType="pubmed">27134008</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>BMC Genomics. 2016 Aug 09;17:582</Citation>
<ArticleIdList><ArticleId IdType="pubmed">27506469</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/PubMed/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000749 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd -nk 000749 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= MersV1 |flux= PubMed |étape= Curation |type= RBID |clé= pubmed:30349558 |texte= Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information. }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Curation/RBID.i -Sk "pubmed:30349558" \ | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd \ | NlmPubMed2Wicri -a MersV1
![]() | This area was generated with Dilib version V0.6.33. | ![]() |