Learning gene regulatory networks from only positive and unlabeled data
<p>Abstract</p> <p>Background</p> <p>Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A stat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2010-05-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/11/228 |