Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate-Induced Rats

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dc.authoridOzhan, Onural/0000-0001-9018-7849
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridParlakpınar, Hakan/0000-0001-9497-3468
dc.authoridKOÇ, AHMET/0000-0003-3484-2137
dc.authoridTaslidere, Elif/0000-0003-1723-2556
dc.authoridAkbulut, Sami/0000-0002-6864-7711
dc.authoridKUCUKAKCALI, ZEYNEP/0000-0001-7956-9272
dc.authorwosidOzhan, Onural/AAE-2356-2020
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidParlakpınar, Hakan/T-6517-2018
dc.authorwosidKOÇ, AHMET/ABI-3322-2020
dc.authorwosidTaslidere, Elif/ABI-8046-2020
dc.authorwosidAkbulut, Sami/L-9568-2014
dc.contributor.authorCicek, Ipek Balikci
dc.contributor.authorColak, Cemil
dc.contributor.authorYologlu, Saim
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorOzhan, Onural
dc.contributor.authorTaslidere, Elif
dc.contributor.authorDanis, Nefsun
dc.date.accessioned2024-08-04T20:54:37Z
dc.date.available2024-08-04T20:54:37Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: The purpose of this study was to carry out the bioinformatic analysis of lncRNA data obtained from the genomic analysis of kidney tissue samples taken from rats with nephrotoxicity induced by methotrexate (MTX) and from rats without pathology and modeling with the tree-based machine learning method. Another aim of the study was to identify potential biomarkers for the diagnosis of nephrotoxicity and to provide a better understanding of the nephrotoxicity formation process by providing the interpretability of the model with explainable artificial intelligence methods as a result of the modeling. Methods: To identify potential indicators of drug-induced nephrotoxicity, 20 female Wistar albino rats were separated into two groups: MTX-treated and the control. Kidney tissue samples were collected from the rats, and genomic, histological, and immunohistochemical analyses were performed. The dataset obtained as a result of genomic analysis was modeled with random forest (RF), a tree-based method. Modeling results were evaluated with sensitivity (Se), specificity (Sp), balanced accuracy (B-Acc), negative predictive value (Npv), accuracy (Acc), positive predictive value (Ppv), and F1-score performance metrics. The local interpretable model-agnostic annotations (LIME) method was used to determine the lncRNAs that could be biomarkers for nephrotoxicity by providing the interpretability of the RF model. Results: The outcomes of the histological and immunohistochemical analyses conducted in the study support the conclusion that MTX use caused kidney injury. According to the results of the bioinformatics analysis, 52 lncRNAs showed different expressions in the groups. As a result of modeling with RF for lncRNAs selected with Boruta variable selection, the B-Acc, Acc, Sp, Se, Npv, Ppv, and F1-score were 88.9%, 90%, 90.9%, 88.9%, 90.9%, 88.9%, and 88.9%, respectively. lncRNAs with id rnaXR_591534.3 rnaXR_005503408.1, rnaXR_005495645.1, rnaXR_001839007.2, rnaXR_005492056.1, and rna_XR_005492522.1. The lncRNAs with the highest variable importance values produced from RF modeling can be used as nephrotoxicity biomarker candidates. Furthermore, according to the LIME results, the high level of lncRNAs with id rnaXR_591534.3 and rnaXR_005503408.1 particularly increased the possibility of nephrotoxicity. Conclusions: With the possible biomarkers resulting from the analyses in this study, it can be ensured that the procedures for the diagnosis of drug-induced nephrotoxicity can be carried out easily, quickly, and effectively.en_US
dc.description.sponsorshipInonu University Scientific Research Projects Coordination Unit [TOA-2021-2593]en_US
dc.description.sponsorshipThe study was supported and funded by the Inonu University Scientific Research Projects Coordination Unit (Project ID: TOA-2021-2593).en_US
dc.identifier.doi10.3390/app13158870
dc.identifier.issn2076-3417
dc.identifier.issue15en_US
dc.identifier.scopus2-s2.0-85167913348en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app13158870
dc.identifier.urihttps://hdl.handle.net/11616/101516
dc.identifier.volume13en_US
dc.identifier.wosWOS:001045417300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectnephrotoxicityen_US
dc.subjectmethotrexateen_US
dc.subjectgenomicsen_US
dc.subjectmachine learningen_US
dc.subjectexplainable artificial intelligenceen_US
dc.subjectbiomarkeren_US
dc.subjectlncRNAen_US
dc.titleNephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate-Induced Ratsen_US
dc.typeArticleen_US

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