Machine learning approaches for multi-omics data integration in medicine

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dc.authorscopusid57211715604
dc.contributor.authorYagin F.H.
dc.date.accessioned2024-08-04T20:03:53Z
dc.date.available2024-08-04T20:03:53Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractCells are a fundamental unit of life, and the ability to study the phenotypes and behavior of cells is crucial to understanding the functioning of complex biological systems. The prognostic and predictive accuracy of disease phenotypes can be enhanced by the use of integrative omics approaches due to their ability to examine biological processes holistically, which could lead to improved treatment and prevention in the long term. Therefore, multi-omics data integration strategies are needed to combine the complementary information brought by each omic layer. A major challenge in multi-omics research for disease diagnosis, monitoring, and treatment options is how to integrate high-dimensional data from omics. This chapter focused on machine learning methods for multi-omics data integration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. All rights reserved.en_US
dc.identifier.doi10.1007/978-3-031-36502-7_3
dc.identifier.endpage38en_US
dc.identifier.isbn9783031365027
dc.identifier.isbn9783031365010
dc.identifier.scopus2-s2.0-85194446694en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage23en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-36502-7_3
dc.identifier.urihttps://hdl.handle.net/11616/92176
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.relation.ispartofMachine Learning Methods for Multi-Omics Data Integrationen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomarker discoveryen_US
dc.subjectDisease diagnosisen_US
dc.subjectDisease subtypingen_US
dc.subjectMachine learningen_US
dc.subjectMulti-Omics data integrationen_US
dc.titleMachine learning approaches for multi-omics data integration in medicineen_US
dc.typeBook Chapteren_US

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