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Bhavnani SK, Eichinger F, Martini S, et al. Network analysis of genes regulated in renal diseases: implications for a molecular-based classification. BMC Bioinformatics. 2009;10:S3doi: 10.1186/1471-2105-10-S9-S3.
Bhavnani, S. K., Eichinger, F., Martini, S., Saxman, P., Jagadish, H. V., & Kretzler, M. (2009). Network analysis of genes regulated in renal diseases: implications for a molecular-based classification. BMC bioinformatics, 10S3. https://doi.org/10.1186/1471-2105-10-S9-S3
Bhavnani, Suresh K, et al. "Network analysis of genes regulated in renal diseases: implications for a molecular-based classification." BMC bioinformatics vol. 10 (2009): S3. doi: https://doi.org/10.1186/1471-2105-10-S9-S3
Bhavnani SK, Eichinger F, Martini S, Saxman P, Jagadish HV, Kretzler M. Network analysis of genes regulated in renal diseases: implications for a molecular-based classification. BMC Bioinformatics. 2009 Sep 17;10:S3. doi: 10.1186/1471-2105-10-S9-S3. PMID: 19761573; PMCID: PMC2745690.
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BMC Bioinformatics. 2009 Sep 17;10:S3. doi: 10.1186/1471-2105-10-S9-S3.

Network analysis of genes regulated in renal diseases: implications for a molecular-based classification.

BMC bioinformatics

Suresh K Bhavnani, Felix Eichinger, Sebastian Martini, Paul Saxman, H V Jagadish, Matthias Kretzler

Affiliations

  1. Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Domino's Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. [email protected]

PMID: 19761573 PMCID: PMC2745690 DOI: 10.1186/1471-2105-10-S9-S3

Abstract

BACKGROUND: Chronic 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.

RESULTS: The 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.

CONCLUSION: The 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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