Modeling Based on Ensemble Learning Methods for Detection of Diagnostic Biomarkers from LncRNA Data in Rats Treated with Cis-Platinum-Induced Hepatotoxicity

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dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridAkbulut, Sami/0000-0002-6864-7711
dc.authoridGozukara Bag, Harika Gozde/0000-0003-1208-4072
dc.authoridParlakpınar, Hakan/0000-0001-9497-3468
dc.authoridKOÇ, AHMET/0000-0003-3484-2137
dc.authoridOzhan, Onural/0000-0001-9018-7849
dc.authoridKUCUKAKCALI, ZEYNEP/0000-0001-7956-9272
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidAkbulut, Sami/L-9568-2014
dc.authorwosidGozukara Bag, Harika Gozde/ABG-7588-2020
dc.authorwosidParlakpınar, Hakan/T-6517-2018
dc.authorwosidKOÇ, AHMET/ABI-3322-2020
dc.authorwosidOzhan, Onural/AAE-2356-2020
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorColak, Cemil
dc.contributor.authorBag, Harika Gozde Gozukara
dc.contributor.authorCicek, Ipek Balikci
dc.contributor.authorOzhan, Onural
dc.contributor.authorYildiz, Azibe
dc.contributor.authorDanis, Nefsun
dc.date.accessioned2024-08-04T20:53:42Z
dc.date.available2024-08-04T20:53:42Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: The first aim of this study is to perform bioinformatic analysis of lncRNAs obtained from liver tissue samples from rats treated with cisplatin hepatotoxicity and without pathology. Another aim is to identify possible biomarkers for the diagnosis/early diagnosis of hepatotoxicity by modeling the data obtained from bioinformatics analysis with ensemble learning methods. Methods: In the study, 20 female Sprague-Dawley rats were divided into a control group and a hepatotoxicity group. Liver samples were taken from rats, and transcriptomic and histopathological analyses were performed. The dataset achieved from the transcriptomic analysis was modeled with ensemble learning methods (stacking, bagging, and boosting). Modeling results were evaluated with accuracy (Acc), balanced accuracy (B-Acc), sensitivity (Se), specificity (Sp), positive predictive value (Ppv), negative predictive value (Npv), and F1 score performance metrics. As a result of the modeling, lncRNAs that could be biomarkers were evaluated with variable importance values. Results: According to histopathological and immunohistochemical analyses, a significant increase was observed in the sinusoidal dilatation and Hsp60 immunoreactivity values in the hepatotoxicity group compared to the control group (p < 0.0001). According to the results of the bioinformatics analysis, 589 lncRNAs showed different expressions in the groups. The stacking model had the best classification performance among the applied ensemble learning models. The Acc, B-Acc, Se, Sp, Ppv, Npv, and F1-score values obtained from this model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. lncRNAs with id rna-XR_005492522.1, rna-XR_005492536.1, and rna-XR_005505831.1 with the highest three values according to the variable importance obtained as a result of stacking modeling can be used as predictive biomarker candidates for hepatotoxicity. Conclusions: Among the ensemble algorithms, the stacking technique yielded higher performance results as compared to the bagging and boosting methods on the transcriptomic data. More comprehensive studies can support the possible biomarkers determined due to the research and the decisive results for the diagnosis of drug-induced hepatotoxicity.en_US
dc.description.sponsorshipInonu University Scientific Research Projects Coordination Unit [TOA-2021-2592]en_US
dc.description.sponsorshipThe study was supported and funded by the Inonu University Scientific Research Projects Coordination Unit (Project ID: TOA-2021-2592).en_US
dc.identifier.doi10.3390/diagnostics13091583
dc.identifier.issn2075-4418
dc.identifier.issue9en_US
dc.identifier.pmid37174973en_US
dc.identifier.scopus2-s2.0-85159223118en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13091583
dc.identifier.urihttps://hdl.handle.net/11616/101350
dc.identifier.volume13en_US
dc.identifier.wosWOS:000986634100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectgenomicen_US
dc.subjecthepatotoxicityen_US
dc.subjectmachine learningen_US
dc.subjectensemble learningen_US
dc.subjectbiomarkeren_US
dc.titleModeling Based on Ensemble Learning Methods for Detection of Diagnostic Biomarkers from LncRNA Data in Rats Treated with Cis-Platinum-Induced Hepatotoxicityen_US
dc.typeArticleen_US

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