ArticleScientific reports2019
A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study.
Article in Scientific reports, 2019. 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
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Integration of high-throughput proteomic data and complementary omics layers with PriOmics.Genome research · 2026Article
- The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.Proteomics · 2025Review
- Dynamic predictors of COVID-19 vaccination uptake and their interconnections over two years in Hong Kong.Nature communications · 2024Article
- Anomaly detection in mixed high-dimensional molecular data.Bioinformatics (Oxford, England) · 2023Article
- DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.Nucleic acids research · 2023Article
- An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types.PLOS digital health · 2022Article
- Integrated Analysis of Gut Microbiome and Lipid Metabolism in Mice Infected with Carbapenem-ResistantMetabolites · 2022Article
- Bucket Fuser: Statistical Signal Extraction for 1DMetabolites · 2022Article
- Educational Attainment Is Associated With Kidney and Cardiovascular Outcomes in the German CKD (GCKD) Cohort.Kidney international reports · 2022Article
- Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses.Metabolites · 2021Review
- Multi-omics integration in biomedical research - A metabolomics-centric review.Analytica chimica acta · 2021Review
- Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools.Biochimica et biophysica acta. Gene regulatory mechanisms · 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
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To this end, omics data are integrated with other data types, e.g., clinical, phenotypic, and demographic parameters of categorical or continuous nature. We exemplify this data integration issue for a chronic kidney disease (CKD) study, comprising complex clinical, demographic, and one-dimensional
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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.