untangling statistical and biological models to understand network inference: the need for a genomics network ontology
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ID: 181111
2014
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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 {it statistical perspective} from a {it mathematical modeling perspective} and elaborate their differences and implications. Our results indicate the imperative need for a {it 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.
| Reference Key |
eemmert-streib2014frontiersuntangling
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|---|---|
| Authors | ;Frank eEmmert-Streib;Matthias eDehmer;Benjamin eHaibe-Kains |
| Journal | chemical record (new york, ny) |
| Year | 2014 |
| DOI |
10.3389/fgene.2014.00299
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| URL | |
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