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Article: Discovering Cancer-Related miRNAs from miRNA-Target Interactions by Support Vector Machines

TitleDiscovering Cancer-Related miRNAs from miRNA-Target Interactions by Support Vector Machines
Authors
Keywordscancer-related miRNAs
support vector machine
dark matters
miRNA-target interactions
expression data
Issue Date2020
PublisherElsevier (Cell Press): OAJ. The Journal's web site is located at http://www.cell.com/molecular-therapy-family/nucleic-acids/latest-content
Citation
Molecular Therapy - Nucleic Acids, 2020, v. 19, p. 1423-1433 How to Cite?
AbstractMicroRNAs (miRNAs) have been shown to be closely related to cancer progression. Traditional methods for discovering cancer-related miRNAs mostly require significant marginal differential expression, but some cancer-related miRNAs may be non-differentially or only weakly differentially expressed. Such miRNAs are called dark matters miRNAs (DM-miRNAs) and are targeted through the Pearson correlation change on miRNA-target interactions (MTIs), but the efficiency of their method heavily relies on restrictive assumptions. In this paper, a novel method was developed to discover DM-miRNAs using support vector machine (SVM) based on not only the miRNA expression data but also the expression of its regulating target. The application of the new method in breast and kidney cancer datasets found, respectively, 9 and 24 potential DM-miRNAs that cannot be detected by previous methods. Eight and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers, respectively, in existing literature. These results indicate that our new method is more effective in discovering cancer-related miRNAs.
Persistent Identifierhttp://hdl.handle.net/10722/290704
ISSN
2023 Impact Factor: 6.5
2023 SCImago Journal Rankings: 1.849
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPian, C-
dc.contributor.authorMao, S-
dc.contributor.authorZhang, G-
dc.contributor.authorDu, J-
dc.contributor.authorLi, F-
dc.contributor.authorLeung, SY-
dc.contributor.authorFan, X-
dc.date.accessioned2020-11-02T05:45:57Z-
dc.date.available2020-11-02T05:45:57Z-
dc.date.issued2020-
dc.identifier.citationMolecular Therapy - Nucleic Acids, 2020, v. 19, p. 1423-1433-
dc.identifier.issn2162-2531-
dc.identifier.urihttp://hdl.handle.net/10722/290704-
dc.description.abstractMicroRNAs (miRNAs) have been shown to be closely related to cancer progression. Traditional methods for discovering cancer-related miRNAs mostly require significant marginal differential expression, but some cancer-related miRNAs may be non-differentially or only weakly differentially expressed. Such miRNAs are called dark matters miRNAs (DM-miRNAs) and are targeted through the Pearson correlation change on miRNA-target interactions (MTIs), but the efficiency of their method heavily relies on restrictive assumptions. In this paper, a novel method was developed to discover DM-miRNAs using support vector machine (SVM) based on not only the miRNA expression data but also the expression of its regulating target. The application of the new method in breast and kidney cancer datasets found, respectively, 9 and 24 potential DM-miRNAs that cannot be detected by previous methods. Eight and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers, respectively, in existing literature. These results indicate that our new method is more effective in discovering cancer-related miRNAs.-
dc.languageeng-
dc.publisherElsevier (Cell Press): OAJ. The Journal's web site is located at http://www.cell.com/molecular-therapy-family/nucleic-acids/latest-content-
dc.relation.ispartofMolecular Therapy - Nucleic Acids-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectcancer-related miRNAs-
dc.subjectsupport vector machine-
dc.subjectdark matters-
dc.subjectmiRNA-target interactions-
dc.subjectexpression data-
dc.titleDiscovering Cancer-Related miRNAs from miRNA-Target Interactions by Support Vector Machines-
dc.typeArticle-
dc.identifier.emailLeung, SY: suetyi@hku.hk-
dc.identifier.authorityLeung, SY=rp00359-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.omtn.2020.01.019-
dc.identifier.pmid32160711-
dc.identifier.pmcidPMC7056629-
dc.identifier.scopuseid_2-s2.0-85080101579-
dc.identifier.hkuros317840-
dc.identifier.volume19-
dc.identifier.spage1423-
dc.identifier.epage1433-
dc.identifier.isiWOS:000519557700116-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl2162-2531-

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