ReviewNature reviews. Nephrology2020
Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.
Review in Nature reviews. Nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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
38 citing papers in PubMed.
- Immune biomarkers, profiles, and responses: a vaccine ontology perspective.Journal of biomedical semantics · 2026Article
- The m5C orchestrator NSUN7 drives SPARC/HMGB1 axis-mediated inflammation to exacerbate kidney injury.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- SEA CDM: Study-Experiment-Assay Common Data Model and Databases for Cross-Domain Data Integration and Analysis.Scientific data · 2026Article
- GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data.bioRxiv : the preprint server for biology · 2025Article
- SLC39A8-mediated zinc dyshomeostasis potentiates kidney disease.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Gene-Disease Relationships in Kidney Genetics.Seminars in nephrology · 2025Review
- Semantic web-based ontology: a comprehensive framework for cardiovascular knowledge representation.BMC cardiovascular disorders · 2025Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules.Health information science and systems · 2024Article
- BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature.Computational and structural biotechnology journal · 2024Article
- The advance of single cell transcriptome to study kidney immune cells in diabetic kidney disease.BMC nephrology · 2024Review
- Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.JMIR medical informatics · 2024Article
- Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms.Heliyon · 2024Article
- Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Article
- Assessment of Recovery Time Effects on Human Primary Neonatal Dermal Fibroblasts After Exposure to Solar-Simulated Ultraviolet Radiation.Journal of lasers in medical sciences · 2024Article
- Integration of artificial intelligence and multi-omics in kidney diseases.Fundamental research · 2023Review
- Present and future directions in diabetic kidney disease.Journal of diabetes and its complications · 2022Review
- A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.Journal of biomedical semantics · 2022Article
- [Nomenclature for kidney function and kidney diseases - Improving assessment and prognosis through precision and comprehensibility].Deutsche medizinische Wochenschrift (1946) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
29 authors.
Funding
Abstract
An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.
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.