The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease

Marta B. Lopes, Roberta Coletti, Flore Duranton, Griet Glorieux, Mayra Alejandra Jaimes Campos, Julie Klein, Matthias Ley, Paul Perco, Alexia Sampri, Aviad Tur-Sinai

Research output: Contribution to journalReview articlepeer-review

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.

Original languageAmerican English
JournalProteomics
Early online date10 Jan 2025
DOIs
StatePublished Online - 10 Jan 2025

Keywords

  • artificial intelligence
  • chronic kidney disease
  • cost-effectiveness
  • machine learning
  • multi-omics

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology

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