The Omics‐Driven Machine Learning Path to Cost‐Effective Precision Medicine in Chronic Kidney Disease
Year: 2025
Type: article
Abstract: Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi-omics datasets, AI can provide real-time, patient-specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI-driven discoveries to ensure their efficacy, relevance, and cost-effectiveness in patient care.
Source: PROTEOMICS
Institutions: NOVAMath Center for Mathematics and Applications, Universidade Nova de Lisboa, Ghent University Hospital, Universitätsklinikum Aachen, Mosaiques Diagnostics and Therapeutics (Germany), RWTH Aachen University, Université Toulouse III - Paul Sabatier, Inserm, Institut des Maladies Métaboliques et Cardiovasculaires, Medical University of Vienna, Innsbruck Medical University, Universität Innsbruck, University of Cambridge, British Heart Foundation, University of Haifa
Language: English
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,650
Funders: Wellcome Trust, Scottish Government, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, European Commission, European Cooperation in Science and Technology, Public Health Agency, Agence Nationale de la Recherche, Österreichische Forschungsförderungsgesellschaft, UNIDEMI, Chief Scientist Office, Scottish Government Health and Social Care Directorate, HORIZON EUROPE Framework Programme, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Fundação para a Ciência e a Tecnologia, Health and Social Care Research and Development Division, NIHR Cambridge Biomedical Research Centre, H2020 Marie Skłodowska-Curie Actions, Health Data Research UK
Awards: G1211085472, Centro de Matemática e Aplicações, G2213036399, G2256342894, G2493601830, G2537968379, Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial - UNIDEMI, Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial - UNIDEMI, G3444286931, Centro de Matemática e Aplicações, G4033576718, G4753630297, G5242062036, G526622128, Large-scale integrative molecular studies of CVDs in multi-ethnic cohorts, G5405653187, G5421189793, G5976648328, G6328501717, G6590358466, G6997658380, The BHF Chair of Epidemiology and Medicine, G8583005300
OpenAlex ID: W4406248679