Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/43460
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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