A Framework Towards Computational Discovery of Disease Sub-types and Associated (Sub-)Biomarkers

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dc.contributor.authorKurnaz, Mehmet Nadir
dc.contributor.authorSeker, Huseyin
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2013
dc.departmentNiğde ÖHÜ
dc.description35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) -- JUL 03-07, 2013 -- Osaka, JAPAN
dc.description.abstractBiomarker related patient data is generally assessed in order to determine relevant but generalized subset of the biomarkers. However, it fails to identify specific sub-groups of the patients or their corresponding (subset of) the biomarkers. This paper therefore proposes a novel framework that is capable of discovering disease sub-groups (types) and associated subset of biomarkers, which is expected to lead to enable the discovery of personalized biomarker set. The framework is based on the utilization of a histogram obtained by using the Euclidean distances between the samples in a given data set. The t-test method is used for the selection of sub-set(s) of the biomarkers whereas the classification is performed by means of k-nearest neighbor, support vector machines and naive Bayes (NBayes) classifiers. For the assessment of the methods, leave-out-out cross validation is employed. As a case study, the method is applied in the analysis of male hypertension microarray data that consists of 159 patients and 22184 gene expressions. The method has helped identify specific subgroups of the patients and their corresponding bio-marker sub-sets. The results therefore suggest that the generalized bio-marker sub-sets are not representative of the disease and therefore more focus should be on the sub-groups of the patients and their biomarker subsets identified through the proposed approach. It is particularly observed that the threshold values over the histogram are crucial to discover both sub-sets of the samples and biomarkers, and therefore can be used to determine complexity level of the study.
dc.description.sponsorshipIEEE Engn Med Biol Soc, Japanese Soc Med & Biol Engn
dc.identifier.endpage4077
dc.identifier.isbn978-1-4577-0216-7
dc.identifier.issn1557-170X
dc.identifier.pmid24110627
dc.identifier.scopus2-s2.0-84886578465
dc.identifier.scopusqualityN/A
dc.identifier.startpage4074
dc.identifier.urihttps://hdl.handle.net/11480/4439
dc.identifier.wosWOS:000341702104130
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
dc.relation.ispartofseriesIEEE Engineering in Medicine and Biology Society Conference Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleA Framework Towards Computational Discovery of Disease Sub-types and Associated (Sub-)Biomarkers
dc.typeConference Object

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