Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy

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dc.authorid0000-0001-8251-4520
dc.contributor.authorAvci, Cigir Biray
dc.contributor.authorBagca, Bakiye Goker
dc.contributor.authorShademan, Behrouz
dc.contributor.authorTakanlou, Leila Sabour
dc.contributor.authorTakanlou, Maryam Sabour
dc.contributor.authorNourazarian, Alireza
dc.date.accessioned2025-04-30T06:45:55Z
dc.date.available2025-04-30T06:45:55Z
dc.date.issued2024
dc.departmentEge Üniversitesi, Tıp Fakültesi, Temel Bilimler Bölümü, Tıbbi Biyoloji Ana Bilim Dalı
dc.description.abstractThis review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.
dc.identifier.citationAvci, C. B., Bagca, B. G., Shademan, B., Takanlou, L. S., Takanlou, M. S., & Nourazarian, A. (2024). Machine learning in oncological pharmacogenomics: Advancing personalized chemotherapy. Functional & Integrative Genomics, 24(5), 182-182.
dc.identifier.doi10.1007/s10142-024-01462-4
dc.identifier.endpage18
dc.identifier.issn1438793X
dc.identifier.issue5
dc.identifier.pmid39365298
dc.identifier.scopusqualityQ3
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1007/s10142-024-01462-4
dc.identifier.urihttps://hdl.handle.net/11454/117184
dc.identifier.volume24
dc.identifier.wosWOS:001328966000002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAvci, Cigir Biray
dc.institutionauthorid0000-0001-8251-4520
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofFunctional & Integrative Genomics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGenetic variability
dc.subjectMachine learning (ML)
dc.subjectOncological pharmacogenomics
dc.subjectPersonalized chemotherapy
dc.subjectTreatment personalization
dc.titleMachine learning in oncological pharmacogenomics: advancing personalized chemotherapy
dc.typeArticle

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