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. 2014 Aug 29:5:299.
doi: 10.3389/fgene.2014.00299. eCollection 2014.

Untangling statistical and biological models to understand network inference: the need for a genomics network ontology

Frank Emmert-Streib  1 , Matthias Dehmer  2 , Benjamin Haibe-Kains  3
Affiliations

Untangling statistical and biological models to understand network inference: the need for a genomics network ontology

Frank Emmert-Streib et al. Front Genet. .

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.

Keywords: computational genomics; epistemology; gene regulatory networks; genomics network ontology; mathematical modeling; statistical inference; systems biology.

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Figures

Figure 1
Figure 1
Schematic comparison of the statistical perspective (red) and the mathematical modeling perspective (blue).
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