ReviewKidney international2012
Systems biology of kidney diseases.
Review in Kidney international, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed, 76 citations in OpenAlex.
- Beyond the Cell Atlas: Functional Communities as the Essential Pathologic Units Driving Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026Review
- Chronic kidney disease and gut microbiota.Heliyon · 2023Review
- Pretransplant kidney transcriptome captures intrinsic donor organ quality and predicts 24-month outcomes.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2022Article
- Revisiting the therapeutic potential of tocotrienol.BioFactors (Oxford, England) · 2022Review
- Review
- Connectivity Mapping Identifies BI-2536 as a Potential Drug to Treat Diabetic Kidney Disease.Diabetes · 2021Article
- The analysis of a time-course transcriptome profile by systems biology approaches reveals key molecular processes in acute kidney injury.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2019Article
- An integrative systems biology approach for precision medicine in diabetic kidney disease.Diabetes, obesity & metabolism · 2018Review
- How Omics Data Can Be Used in Nephrology.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2018Review
- Proteo-metabolomics reveals compensation between ischemic and non-injured contralateral kidneys after reperfusion.Scientific reports · 2018Article
- A Systems-Level View of Renal Metabolomics.Seminars in nephrology · 2018Review
- Inflammation-Related Mechanisms in Chronic Kidney Disease Prediction, Progression, and Outcome.Journal of immunology research · 2018Review
- Digital pathology in nephrology clinical trials, research, and pathology practice.Current opinion in nephrology and hypertension · 2017Review
- Integrating 3-omics data analyze rat lung tissue of COPD states and medical intervention by delineation of molecular and pathway alterations.Bioscience reports · 2017Article
- Clinical proteomics in kidney disease as an exponential technology: heading towards the disruptive phase.Clinical kidney journal · 2017Article
- The nephrologist of tomorrow: towards a kidney-omic future.Pediatric nephrology (Berlin, Germany) · 2017Review
- Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies.Translational oncology · 2016Review
- Epigenetics in Kidney Transplantation: Current Evidence, Predictions, and Future Research Directions.Transplantation · 2016Review
- Integrative Biology of Diabetic Kidney Disease.Kidney diseases (Basel, Switzerland) · 2015Article
- Nephrology research--the past, present and future.Nature reviews. Nephrology · 2015Review
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 1 institution in 1 country.
Funding
Abstract
Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. The mechanisms underlying the origins and progression of kidney diseases are not fully understood. Multiple factors involved in the pathogenesis of kidney diseases have made the traditional candidate gene approach of limited value toward full understanding of the molecular mechanisms of these diseases. A systems biology approach that integrates computational modeling with large-scale data gathering of the molecular changes could be useful in identifying the multiple interacting genes and their products that drive kidney diseases. Advances in biotechnology now make it possible to gather large data sets to characterize the role of the genome, epigenome, transcriptome, proteome, and metabolome in kidney diseases. When combined with computational analyses, these experimental approaches will provide a comprehensive understanding of the underlying biological processes. Multiscale analysis that connects the molecular interactions and cell biology of different kidney cells to renal physiology and pathology can be utilized to identify modules of biological and clinical importance that are perturbed in disease processes. This integration of experimental approaches and computational modeling is expected to generate new knowledge that can help to identify marker sets to guide the diagnosis, monitor disease progression, and identify new therapeutic targets.
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.