Genomic biomarker discovery in disease progression and therapy response in bladder cancer utilizing machine learning

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dc.authorid0000-0001-6657-9738
dc.contributor.authorLiosis, Konstantinos Christos
dc.contributor.authorMarouf, Ahmed Al
dc.contributor.authorRokne, Jon G.
dc.contributor.authorGhosh, Sunita
dc.contributor.authorBismar, Tarek A.
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2023-10-23T08:31:59Z
dc.date.available2023-10-23T08:31:59Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractCancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan–Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner.
dc.identifier.citationLiosis, K. C., Marouf, A. A., Rokne, J. G., Ghosh, S., Bismar, T. A. ve Alhajj, R. (2023). Genomic biomarker discovery in disease progression and therapy response in bladder cancer utilizing machine learning. Cancers, 15(19). https://doi.org/10.3390/cancers15194801
dc.identifier.doi10.3390/cancers15194801
dc.identifier.issn2072-6694
dc.identifier.issue19
dc.identifier.pmid37835496
dc.identifier.scopus2-s2.0-85173820688
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/cancers15194801
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11622
dc.identifier.volume15
dc.identifier.wos001084852800001en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAlhajj, Reda
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofCancersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectBioinformatics Analysis
dc.subjectBladder Cancer
dc.subjectDisease Progression
dc.subjectElastic-Net
dc.subjectGenomic Biomarker Discovery
dc.subjectTherapy Response
dc.titleGenomic biomarker discovery in disease progression and therapy response in bladder cancer utilizing machine learning
dc.typeArticle

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