Title |
Mouse obesity network reconstruction with a variational Bayes algorithm to employ aggressive false positive control
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Published in |
BMC Bioinformatics, April 2012
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DOI | 10.1186/1471-2105-13-53 |
Pubmed ID | |
Authors |
Benjamin A Logsdon, Gabriel E Hoffman, Jason G Mezey |
Abstract |
We propose a novel variational Bayes network reconstruction algorithm to extract the most relevant disease factors from high-throughput genomic data-sets. Our algorithm is the only scalable method for regularized network recovery that employs Bayesian model averaging and that can internally estimate an appropriate level of sparsity to ensure few false positives enter the model without the need for cross-validation or a model selection criterion. We use our algorithm to characterize the effect of genetic markers and liver gene expression traits on mouse obesity related phenotypes, including weight, cholesterol, glucose, and free fatty acid levels, in an experiment previously used for discovery and validation of network connections: an F2 intercross between the C57BL/6 J and C3H/HeJ mouse strains, where apolipoprotein E is null on the background. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Brazil | 1 | 2% |
United Kingdom | 1 | 2% |
Sweden | 1 | 2% |
Taiwan | 1 | 2% |
Poland | 1 | 2% |
Unknown | 51 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 19 | 33% |
Researcher | 11 | 19% |
Professor > Associate Professor | 7 | 12% |
Student > Master | 5 | 9% |
Student > Bachelor | 2 | 3% |
Other | 4 | 7% |
Unknown | 10 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 25 | 43% |
Computer Science | 11 | 19% |
Biochemistry, Genetics and Molecular Biology | 4 | 7% |
Engineering | 2 | 3% |
Medicine and Dentistry | 2 | 3% |
Other | 1 | 2% |
Unknown | 13 | 22% |