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Mendeley readers
| Title |
Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies
|
|---|---|
| Published in |
BMC Bioinformatics, November 2010
|
| DOI | 10.1186/1471-2105-11-552 |
| Pubmed ID | |
| Authors |
Bo Chen, Minhua Chen, John Paisley, Aimee Zaas, Christopher Woods, Geoffrey S Ginsburg, Alfred Hero, Joseph Lucas, David Dunson, Lawrence Carin |
| Abstract |
Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 5% |
| Brazil | 1 | 1% |
| Australia | 1 | 1% |
| Taiwan | 1 | 1% |
| Israel | 1 | 1% |
| Unknown | 66 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 27% |
| Researcher | 15 | 20% |
| Student > Master | 9 | 12% |
| Student > Bachelor | 6 | 8% |
| Professor | 6 | 8% |
| Other | 13 | 18% |
| Unknown | 5 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 20% |
| Engineering | 9 | 12% |
| Computer Science | 9 | 12% |
| Mathematics | 8 | 11% |
| Medicine and Dentistry | 8 | 11% |
| Other | 18 | 24% |
| Unknown | 7 | 9% |