ReviewKidney international2020
Machine learning, the kidney, and genotype-phenotype analysis.
Review in Kidney international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- GADD45A may be a potential biomarker associated with endoplasmic reticulum stress in focal segmental glomerulosclerosis.Biochemistry and biophysics reports · 2026Article
- The lipid-podocyte axis: emerging clues in membranous nephropathy pathogenesis.Frontiers in medicine · 2026Review
- Machine learning for predicting the prognosis of patients with thymoma and thymic carcinoma.Journal of thoracic disease · 2025Article
- The data-intensive research paradigm: challenges and responses in clinical professional graduate education.Frontiers in medicine · 2025Review
- Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database.Heliyon · 2024Article
- Integrated multi-omics with machine learning to uncover the intricacies of kidney disease.Briefings in bioinformatics · 2024Review
- Effects of environmental phenols on eGFR: machine learning modeling methods applied to cross-sectional studies.Frontiers in public health · 2024Article
- Metabolomics: A useful tool for ischemic stroke research.Journal of pharmaceutical analysis · 2023Review
- Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease.European radiology · 2023Article
- Integration of artificial intelligence and multi-omics in kidney diseases.Fundamental research · 2023Review
- Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease.NPJ digital medicine · 2022Article
- Advances in proteomic profiling of pediatric kidney diseases.Pediatric nephrology (Berlin, Germany) · 2022Review
- Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype-phenotype interactions.Frontiers in genetics · 2022Article
- How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?Seminars in immunopathology · 2021Review
- Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.The American journal of pathology · 2021Article
- Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods.BMC bioinformatics · 2021Article
- Integrated multi-omics approaches to improve classification of chronic kidney disease.Nature reviews. Nephrology · 2020Review
- Nephrology Lagging Behind in Machine Learning Utilization.Kidney medicineArticle
- Machine Learning Applications in Nephrology: A Bibliometric Analysis Comparing Kidney Studies to Other Medicine Subspecialities.Kidney medicineArticle
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.
Funding
Abstract
With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.
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.