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Article: On Predicting Epithelial Mesenchymal Transition by Integrating RNA-binding Proteins and Correlation Data via L1/2-Regularization Method

TitleOn Predicting Epithelial Mesenchymal Transition by Integrating RNA-binding Proteins and Correlation Data via L1/2-Regularization Method
Authors
KeywordsL1/2-regularization
Classification
RNA-binding proteins (RBPs)
Epithelial-mesenchymal transition (EMT)
Issue Date2019
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmed
Citation
Artificial Intelligence in Medicine, 2019, v. 95, p. 96-103 How to Cite?
AbstractIdentifying tumor metastasis signatures from gene expression data at the whole genome level remains an arduous challenge, particularly so when the number of genes is huge and the number of experimental samples is small. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than on tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. We apply an extended LASSO model, L1/2-regularization model, as a feature selector, to identify significant RNA-binding proteins (RBPs) that contribute to regulating the EMT. We find that the L1/2-regularization model significantly outperforms LASSO in the EMT regulation problem. Furthermore, remarkable improvement in L1/2-regularization model classification performance can be achieved by incorporating extra information, specifically correlation values. We demonstrate that the L1/2-regularization model is applicable for identifying significant RBPs in biological research. Identified RBPs will facilitate study of the underlying mechanisms of the EMT.
Persistent Identifierhttp://hdl.handle.net/10722/275051
ISSN
2017 Impact Factor: 2.879
2015 SCImago Journal Rankings: 0.884
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorQiu, Y-
dc.contributor.authorJiang, H-
dc.contributor.authorChing, WK-
dc.contributor.authorNg, MK-
dc.date.accessioned2019-09-10T02:34:26Z-
dc.date.available2019-09-10T02:34:26Z-
dc.date.issued2019-
dc.identifier.citationArtificial Intelligence in Medicine, 2019, v. 95, p. 96-103-
dc.identifier.issn0933-3657-
dc.identifier.urihttp://hdl.handle.net/10722/275051-
dc.description.abstractIdentifying tumor metastasis signatures from gene expression data at the whole genome level remains an arduous challenge, particularly so when the number of genes is huge and the number of experimental samples is small. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than on tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. We apply an extended LASSO model, L1/2-regularization model, as a feature selector, to identify significant RNA-binding proteins (RBPs) that contribute to regulating the EMT. We find that the L1/2-regularization model significantly outperforms LASSO in the EMT regulation problem. Furthermore, remarkable improvement in L1/2-regularization model classification performance can be achieved by incorporating extra information, specifically correlation values. We demonstrate that the L1/2-regularization model is applicable for identifying significant RBPs in biological research. Identified RBPs will facilitate study of the underlying mechanisms of the EMT.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/artmed-
dc.relation.ispartofArtificial Intelligence in Medicine-
dc.subjectL1/2-regularization-
dc.subjectClassification-
dc.subjectRNA-binding proteins (RBPs)-
dc.subjectEpithelial-mesenchymal transition (EMT)-
dc.titleOn Predicting Epithelial Mesenchymal Transition by Integrating RNA-binding Proteins and Correlation Data via L1/2-Regularization Method-
dc.typeArticle-
dc.identifier.emailChing, WK: wching@hku.hk-
dc.identifier.emailNg, MK: kkpong@hku.hk-
dc.identifier.authorityChing, WK=rp00679-
dc.identifier.authorityNg, MK=rp02578-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.artmed.2018.09.005-
dc.identifier.pmid30352711-
dc.identifier.scopuseid_2-s2.0-85055125740-
dc.identifier.hkuros303659-
dc.identifier.volume95-
dc.identifier.spage96-
dc.identifier.epage103-
dc.identifier.isiWOS:000464091700009-
dc.publisher.placeNetherlands-

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