geneSurv: An interactive web-based tool for survival analysis in genomics research

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dc.authoridGOKSULUK, DINCER/0000-0002-2752-7668
dc.authoridKorkmaz, Selçuk/0000-0003-4632-6850
dc.authoridZARARSIZ, GOKMEN/0000-0001-5801-1835
dc.authoridZararsız, Gökmen/0000-0001-5801-1835
dc.authorwosidGOKSULUK, DINCER/E-9175-2013
dc.authorwosidKorkmaz, Selçuk/AAU-4677-2020
dc.authorwosidZARARSIZ, GOKMEN/ABH-7959-2020
dc.authorwosidZararsız, Gökmen/E-8818-2013
dc.contributor.authorKorkmaz, Selcuk
dc.contributor.authorGoksuluk, Dincer
dc.contributor.authorZararsiz, Gokmen
dc.contributor.authorKarahan, Sevilay
dc.date.accessioned2024-06-12T11:15:54Z
dc.date.available2024-06-12T11:15:54Z
dc.date.issued2017
dc.departmentTrakya Üniversitesien_US
dc.description.abstractSurvival analysis methods are often used in cancer studies. It has been shown that the combination of clinical data with genomics increases the predictive performance of survival analysis methods. But, this leads to a high-dimensional data problem. Fortunately, new methods have been developed in the last decade to overcome this problem. However, there is a strong need for easily accessible, user-friendly and interactive tool to perform survival analysis in the presence of genomics data. We developed an open-source and freely available web-based tool for survival analysis methods that can deal with high-dimensional data. This tool includes classical methods, such as Kaplan-Meier, Cox proportional hazards regression, and advanced methods, such as penalized Cox regression and Random Survival Forests. It also offers an optimal cutoff determination method based on maximizing several test statistics. The tool has a simple and interactive interface, and it can handle high dimensional data through feature selection and ensemble methods. To dichotomize gene expressions, geneSurv can identify optimal cutoff points. Users can upload their microarray, RNA-Seq, chip-Seq, proteomics, metabolomics or clinical data as a nxp dimensional data matrix, where n refers to samples and p refers to genes. This tool is available free at www.biosoft.hacettepe.edu.tr/geneSurv. All source code is available at https://github.com/selcukorkmaz/geneSurv under the GPL-3 license.en_US
dc.identifier.doi10.1016/j.compbiomed.2017.08.031
dc.identifier.endpage496en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid28889076en_US
dc.identifier.scopus2-s2.0-85028937468en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage487en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2017.08.031
dc.identifier.urihttps://hdl.handle.net/20.500.14551/24108
dc.identifier.volume89en_US
dc.identifier.wosWOS:000413376600046en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers In Biology And Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSurvival Analysisen_US
dc.subjectGenomicsen_US
dc.subjectCox Regressionen_US
dc.subjectKaplan-Meieren_US
dc.subjectCutoffen_US
dc.subjectFeature Selectionen_US
dc.subjectRandom Forestsen_US
dc.subjectCanceren_US
dc.subjectSelectionen_US
dc.subjectClassificationen_US
dc.subjectRegressionen_US
dc.subjectRegularizationen_US
dc.subjectValidationen_US
dc.subjectExpressionen_US
dc.subjectTestsen_US
dc.subjectModelen_US
dc.titlegeneSurv: An interactive web-based tool for survival analysis in genomics researchen_US
dc.typeArticleen_US

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