title: Untangling statistical and biological models to understand network inference: the need for a genomics network ontology.
abstract: 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.
authors.affiliation: Computational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast Belfast, UK.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
authors.affiliation: Institute for Bioinformatics and Translational Research, UMIT Hall in Tyrol, Austria.
authors.forename: Matthias
authors.initials: M
authors.lastname: Dehmer
authors.affiliation: Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Centre, University Health Network Toronto, ON, Canada.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haibe-Kains
keywords: computational genomics; epistemology; gene regulatory networks; genomics network ontology; mathematical modeling; statistical inference; systems biology