ArticleMetabolomics : Official journal of the Metabolomic Society2019
Machine learning distilled metabolite biomarkers for early stage renal injury.
Article in Metabolomics : Official journal of the Metabolomic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Enhancing end-stage renal disease outcome prediction: a multisourced data-driven approach.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.Proteomics · 2025Review
- Design and validation of an automated healthcare-integrated biobanking algorithm for identification of advanced chronic kidney disease.Computational and structural biotechnology journal · 2025Article
- Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Artificial intelligence for the prevention and prediction of colorectal neoplasms.Journal of translational medicine · 2023Article
- Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence.Journal of clinical medicine · 2023Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Serum Metabolites Associated with Blood Pressure in Chronic Kidney Disease Patients.Metabolites · 2022Article
- Applications of machine learning in metabolomics: Disease modeling and classification.Frontiers in genetics · 2022Review
- New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature.International journal of molecular sciences · 2020Review
- Machine Learning Applications for Mass Spectrometry-Based Metabolomics.Metabolites · 2020Review
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWith chronic kidney disease (CKD), kidney becomes damaged overtime and fails to clean blood. Around 15% of US adults have CKD and nine in ten adults with CKD do not know they have it.
objectiveEarly prediction and accurate monitoring of CKD may improve care and decrease the frequent progression to end-stage renal disease. There is an urgent demand to discover specific biomarkers that allow for monitoring of early-stage CKD, and response to treatment.
methodTo discover such biomarkers, shotgun high throughput was applied to the detection of serum metabolites biomarker discovery for early stages of CKD from 703 participants. Ultra performance liquid chromatography coupled with high-definition mass spectrometry (UPLC-HDMS)-based metabolomics was used for the determination of 703 fasting serum samples from five stages of CKD patients and age-matched healthy controls. RESULTS AND
conclusionWe discovered a set of metabolite biomarkers using a series of classic and neural network based machine learning techniques. This set of metabolites can separate early CKD stage patents from normal subjects with high accuracy. Our study illustrates the power of machine learning methods in metabolite biomarker study.
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.