| One of the main topics in systems biology is to model genetic pathways. Genes of a pathway, which show linear dependencies of their expression values, are easy to identify to belong to the pathway. However, if feedback loops or signal cascades are present, gene expression values of pathway genes can be nonlinearly dependent on the expression values of other genes in the pathway. In this situation such genes are hard to detect as belonging to the pathway because nonlinearity and noise must be distinguished.

We propose an algorithm to infer nonlinear network elements in pathways from microarray data. Our model assumes, that gene expression values, belonging to one pathway, are mainly driven by one single latent factor. We... |
