Title |
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
|
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Published in |
Genome Biology, May 2006
|
DOI | 10.1186/gb-2006-7-5-r36 |
Pubmed ID | |
Authors |
Richard Bonneau, David J Reiss, Paul Shannon, Marc Facciotti, Leroy Hood, Nitin S Baliga, Vesteinn Thorsson |
Abstract |
We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 4 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 50% |
Members of the public | 2 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 28 | 6% |
United Kingdom | 8 | 2% |
Belgium | 3 | <1% |
Sweden | 3 | <1% |
Brazil | 3 | <1% |
Switzerland | 2 | <1% |
Japan | 2 | <1% |
Mexico | 2 | <1% |
Italy | 1 | <1% |
Other | 10 | 2% |
Unknown | 429 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 143 | 29% |
Researcher | 113 | 23% |
Student > Master | 43 | 9% |
Professor > Associate Professor | 38 | 8% |
Student > Bachelor | 33 | 7% |
Other | 75 | 15% |
Unknown | 46 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 208 | 42% |
Biochemistry, Genetics and Molecular Biology | 80 | 16% |
Computer Science | 68 | 14% |
Engineering | 22 | 4% |
Physics and Astronomy | 14 | 3% |
Other | 49 | 10% |
Unknown | 50 | 10% |