ArticleBMC bioinformatics2009
Network analysis of genes regulated in renal diseases: implications for a molecular-based classification.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 35 citations in OpenAlex.
- Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study.Journal of medical Internet research · 2025Article
- Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.Kidney international · 2024Article
- Subtyping Social Determinants of Health inmedRxiv : the preprint server for health sciences · 2023Article
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.JMIR medical informatics · 2022Article
- Temporal profile of the renal transcriptome of HIV-1 transgenic mice during disease progression.PloS one · 2014Article
- Systems biology of kidney diseases.Kidney international · 2012Review
- Formal concept analysis of disease similarity.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2012Article
- Periostin is induced in glomerular injury and expressed de novo in interstitial renal fibrosis.The American journal of pathology · 2011Article
- Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations.BMC research notes · 2010Article
- FragViz: visualization of fragmented networks.BMC bioinformatics · 2010Article
- Genetic risk factors in lupus nephritis and IgA nephropathy--no support of an overlap.PloS one · 2010Article
- Integrated network analysis of transcriptomic and proteomic data in psoriasis.BMC systems biology · 2010Article
- Selected proceedings of the 2009 Summit on Translational Bioinformatics.BMC bioinformatics · 2009Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundChronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases.
resultsThe analysis revealed three main regularities: (1) Many genes associated with a single disease, and fewer genes associated with many diseases. (2) Unexpected combinations of renal diseases that share relatively large numbers of genes. (3) Uniform concordance in the regulation of all genes in the network.
conclusionThe overall results suggest the need to define a molecular-based classification of renal diseases, in addition to hypotheses for the unexpected patterns of shared genes and the uniformity in gene concordance. Furthermore, the results demonstrate the utility of network analyses to rapidly understand complex relationships between diseases and regulated genes.
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