Title |
Meta-analysis of molecular response of kidney to ischemia reperfusion injury for the identification of new candidate genes
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Published in |
BMC Nephrology, October 2013
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DOI | 10.1186/1471-2369-14-231 |
Pubmed ID | |
Authors |
Dmitry N Grigoryev, Dilyara I Cheranova, Daniel P Heruth, Peixin Huang, Li Q Zhang, Hamid Rabb, Shui Q Ye |
Abstract |
Accumulated to-date microarray data on ischemia reperfusion injury (IRI) of kidney represent a powerful source for identifying new targets and mechanisms of kidney IRI. In this study, we conducted a meta-analysis of gene expression profiles of kidney IRI in human, pig, rat, and mouse models, using a new scoring method to correct for the bias of overrepresented species. The gene expression profiles were obtained from the public repositories for 24 different models. After filtering against inclusion criteria 21 experimental settings were selected for meta-analysis and were represented by 11 rat models, 6 mouse models, and 2 models each for pig and human, with a total of 150 samples. Meta-analysis was conducted using expression-based genome-wide association study (eGWAS). The eGWAS results were corrected for a rodent species bias using a new weighted scoring algorithm, which favors genes with unidirectional change in expression in all tested species. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 26 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 5 | 19% |
Student > Ph. D. Student | 4 | 15% |
Professor > Associate Professor | 3 | 12% |
Other | 3 | 12% |
Student > Doctoral Student | 2 | 8% |
Other | 5 | 19% |
Unknown | 4 | 15% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 12 | 46% |
Agricultural and Biological Sciences | 5 | 19% |
Computer Science | 1 | 4% |
Biochemistry, Genetics and Molecular Biology | 1 | 4% |
Social Sciences | 1 | 4% |
Other | 1 | 4% |
Unknown | 5 | 19% |