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ROSIE

2022-05Artigo científico Acesso aberto
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dc.contributor.authorJensch, Antje
dc.contributor.authorLopes, Marta B.
dc.contributor.authorVinga, Susana
dc.contributor.authorRadde, Nicole
dc.contributor.institutionNOVALincs
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.pblSAGE Publications
dc.date.accessioned2022-09-07T22:28:22Z
dc.date.available2022-09-07T22:28:22Z
dc.date.issued2022-05
dc.descriptionWe thank Peter Segaert for providing his adapted code of the enetLTS method. The results presented here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga . Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 390740016 (AJ and NR). Publisher Copyright: © The Author(s) 2022.
dc.description.abstractThe extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of (Formula presented.) genes and more than (Formula presented.) samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent12
dc.format.extent2460153
dc.identifier.doi10.1177/09622802211072456
dc.identifier.issn0962-2802
dc.identifier.otherPURE: 42456000
dc.identifier.otherPURE UUID: d6bc2daf-ec70-4b28-9902-eaa170cc6f7e
dc.identifier.otherScopus: 85123985352
dc.identifier.otherPubMed: 35072570
dc.identifier.otherWOS: 000751405800001
dc.identifier.otherORCID: /0000-0002-4135-1857/work/120011534
dc.identifier.urihttp://hdl.handle.net/10362/143575
dc.identifier.urlhttps://www.scopus.com/pages/publications/85123985352
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/CEEC INST 2018/CEECINST%2F00102%2F2018%2FCP1567%2FCT0001/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.relationCenter for Mathematics and Applications
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relationMulti-omic networks in gliomas
dc.relationOLISSIPO – Fostering Computational Biology Research and Innovation in Lisbon
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-BIO%2F4180%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/951970/EU
dc.subjectbiomarker
dc.subjectclassification
dc.subjectEnsemble
dc.subjectfeature selection
dc.subjectoutlier
dc.subjectrobust
dc.subjectsparse
dc.subjecttriple-Negative Breast Cancer
dc.subjectEpidemiology
dc.subjectStatistics and Probability
dc.subjectHealth Information Management
dc.subjectSDG 3 - Good Health and Well-being
dc.titleROSIEen
dc.title.subtitleRObust Sparse ensemble for outlIEr detection and gene selection in cancer omics dataen
dc.typejournal article
degois.publication.firstPage947
degois.publication.issue5
degois.publication.lastPage958
degois.publication.titleStatistical Methods In Medical Research
degois.publication.volume31
dspace.entity.typePublication
oaire.awardNumberUIDB/04516/2020
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oaire.awardNumberUIDB/50022/2020
oaire.awardNumberUIDB/50021/2020
oaire.awardNumberPTDC/CCI-BIO/4180/2020
oaire.awardNumber951970
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardTitleMulti-omic networks in gliomas
oaire.awardTitleOLISSIPO – Fostering Computational Biology Research and Innovation in Lisbon
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-BIO%2F4180%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/951970/EU
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
oaire.fundingStreamH2020
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.nameFundação para a Ciência e a Tecnologia
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project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameEuropean Commission
rcaap.rightsopenAccess
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