Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection

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dc.authoridCakir, Altan/0000-0002-8627-7689|Ulucan, Ozlem/0000-0002-7442-5728|Cakir, Arzu/0000-0002-5685-385X|tuncer, melisa/0000-0002-7364-9340
dc.authorwosidCakir, Altan/ABD-4450-2020
dc.authorwosidUlucan, Ozlem/K-8410-2018
dc.contributor.authorCakir, Arzu
dc.contributor.authorTuncer, Melisa
dc.contributor.authorTaymaz-Nikerel, Hilal
dc.contributor.authorUlucan, Ozlem
dc.date.accessioned2024-07-18T20:56:57Z
dc.date.available2024-07-18T20:56:57Z
dc.date.issued2021
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractOne in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.en_US
dc.description.sponsorshipTUBITAK [(2209-A)-1919B011902354]en_US
dc.description.sponsorshipThis study has been supported by TUBITAK (2209-A)-1919B011902354.en_US
dc.identifier.doi10.1038/s41397-021-00246-4
dc.identifier.endpage681en_US
dc.identifier.issn1470-269X
dc.identifier.issn1473-1150
dc.identifier.issue6en_US
dc.identifier.pmid34155353en_US
dc.identifier.scopus2-s2.0-85108414895en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage673en_US
dc.identifier.urihttps://doi.org/10.1038/s41397-021-00246-4
dc.identifier.urihttps://hdl.handle.net/11411/8923
dc.identifier.volume21en_US
dc.identifier.wosWOS:000664007100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringernatureen_US
dc.relation.ispartofPharmacogenomics Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChemotherapyen_US
dc.subjectCanceren_US
dc.subjectClassificationen_US
dc.subjectProteinsen_US
dc.subjectAlopeciaen_US
dc.subjectSuccessen_US
dc.subjectPackageen_US
dc.subjectDamageen_US
dc.subjectToolen_US
dc.titleSide effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selectionen_US
dc.typeArticleen_US

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