Untangling statistical and biological models to understand network inference: The need for a genomics network ontology
Research output: Contribution to journal › Article › Scientific › peer-review
|Article number||article 229|
|Journal||Frontiers in Genetics|
|Publication status||Published - 2014|
|Publication type||A1 Journal article-refereed|
In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.