ReviewProteomics2025
The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.
Review in Proteomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods.Frontiers in pediatrics · 2025Pooled it
- Emerging Urinary Biomarkers and Innovative Technologies for the Early Detection and Personalized Management of Chronic Kidney Disease.International journal of molecular sciences · 2026Review
- Rejection-Focused Precision Medicine in Kidney Transplantation: Biology, Biomarkers, and Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- NMR-based metabolomics in a clinical cohort: deciphering the metabolic characteristics of gout with the dampness-heat syndrome and elucidate the efficacy of Simiao Pill.Chinese medicine · 2026Article
- From albuminuria to multi-omics signatures: emerging biomarkers and drug targets for early-stage chronic kidney disease.Frontiers in pharmacology · 2026Review
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Multi-omics and machine learning reveal LYZ and ISG15 as diagnostic and therapeutic targets in autoimmune-mediated chronic kidney disease.Frontiers in immunology · 2026Article
- Integrative multi-omics profiling for early diagnosis, stratification and personalized management of chronic kidney disease: a new paradigm.Clinical and experimental medicine · 2025Review
- A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.Renal failure · 2025Article
- Artificial intelligence, machine learning, telemedicine, and digital transformation in nephrology and transplantation.Renal failure · 2025Article
- Autophagy-senescence interplay in kidney disease: mechanistic insights and therapeutic potential.Molecular biology reports · 2025Review
- Integrative Analysis of Drug Co-Prescriptions in Peritoneal Dialysis Reveals Molecular Targets and Novel Strategies for Intervention.Journal of clinical medicine · 2025Article
- Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI.Diagnostics (Basel, Switzerland) · 2025Review
- The role and targeting strategies of non-coding RNAs in immunotherapy resistance in oral squamous cell carcinoma.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.