Learning gene regulatory networks from only positive and unlabeled data

Background: 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 statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact. Results: A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show th...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Luigi Cerulo, Charles Elkan, Michele Ceccarelli
• Keywords: BMCBI 2010, Gene Regulatory Networks, Regulatory Connections, Unsupervised Methods