AUCTSP: an improved biomarker gene pair class predictor

dc.contributor.authorKagaris, Dimitri
dc.contributor.authorKhamesipour, Alireza
dc.contributor.authorYiannoutsos, Constantin T.
dc.contributor.departmentBiostatistics, School of Public Healthen_US
dc.date.accessioned2019-05-20T19:15:13Z
dc.date.available2019-05-20T19:15:13Z
dc.date.issued2018-06-26
dc.description.abstractThe Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions ("pivot" genes). RESULTS: We introduce the AUCTSP classifier as an alternative rank-based estimator of the magnitude of the ranking reversals involved in the original TSP. The proposed estimator is based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and as such, takes into account the separation of the entire distribution of gene expression levels in gene pairs under the conditions considered, as opposed to comparing gene rankings within individual subjects as in the original TSP formulation. Through extensive simulations and case studies involving classification in ovarian, leukemia, colon, breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative (pivot) genes. CONCLUSIONS: The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across all subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationKagaris, D., Khamesipour, A., & Yiannoutsos, C. T. (2018). AUCTSP: an improved biomarker gene pair class predictor. BMC bioinformatics, 19(1), 244. doi:10.1186/s12859-018-2231-1en_US
dc.identifier.urihttps://hdl.handle.net/1805/19385
dc.language.isoen_USen_US
dc.publisherBMCen_US
dc.relation.isversionof10.1186/s12859-018-2231-1en_US
dc.relation.journalBMC Bioinformaticsen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.sourcePMCen_US
dc.subjectAUCen_US
dc.subjectBreast canceren_US
dc.subjectColon canceren_US
dc.subjectDiffuse large B-Cell lymphomaen_US
dc.subjectGene expressionen_US
dc.subjectGene selectionen_US
dc.subjectLeukemiaen_US
dc.subjectMicroarray data analysisen_US
dc.subjectOvarian canceren_US
dc.subjectProstate canceren_US
dc.subjectReceiver operating characteristic (ROC) curveen_US
dc.titleAUCTSP: an improved biomarker gene pair class predictoren_US
dc.typeArticleen_US
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