| Title |
Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge
|
|---|---|
| Published in |
PLOS ONE, February 2010
|
| DOI | 10.1371/journal.pone.0009134 |
| Pubmed ID | |
| Authors |
Mika Gustafsson, Michael Hörnquist |
| Abstract |
To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 8% |
| United Kingdom | 2 | 3% |
| Hong Kong | 1 | 2% |
| Sweden | 1 | 2% |
| Unknown | 52 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 34% |
| Researcher | 19 | 31% |
| Student > Master | 9 | 15% |
| Other | 4 | 7% |
| Student > Postgraduate | 3 | 5% |
| Other | 4 | 7% |
| Unknown | 1 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 38% |
| Computer Science | 13 | 21% |
| Biochemistry, Genetics and Molecular Biology | 8 | 13% |
| Medicine and Dentistry | 4 | 7% |
| Mathematics | 3 | 5% |
| Other | 9 | 15% |
| Unknown | 1 | 2% |