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Title: | Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy | Authors: | Coskun, Abdurrahman Ertaylan, Gokhan PUSPARUM, Murih VAN HOOF, Rebekka Kaya, Zelal Zuhal Khosravi, Arezoo Zarrabi, Ali |
Issue Date: | 2024 | Publisher: | ELSEVIER | Source: | BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE, 1870 (7) (Art N° 167339) | Abstract: | Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies. | Keywords: | Genomics;Global health;Metabolomics;Nano-omics;Personalized medicine | Document URI: | http://hdl.handle.net/1942/43460 | ISSN: | 0925-4439 | e-ISSN: | 1879-260X | DOI: | 10.1016/j.bbadis.2024.167339 | ISI #: | WOS:001270413800001 | Rights: | 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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Advancing personalized medicine_ Integrating statistical algorithms with omics and nano-omics for enhanced.pdf | Published version | 2.8 MB | Adobe PDF | View/Open |
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