Integration of molecular network data reconstructs Gene Ontology.
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Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature.In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO.Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling.
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Affiliation: Department of Computing, Imperial College London SW7 2AZ, UK.
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btu470-F1: Schematic representation of datasets used in this study. Two types of objects are represented: genes interconnected via four types of interaction networks (PPI, GI, Co-Ex and YeastNet) and GO terms interconnected via directed semantic relations from GO hierarchy Mentions: Annotation files from GO are used to construct the binary relation matrix, , with entries if gene i is annotated by GO term j and 0 otherwise. For each of the aforementioned biological networks, we also compute GDV similarity constraint matrices: . As we describe in Section 2.2, we only consider gene pairs with statistically significant GDV similarity. All these network data are schematically represented in Figure 1.Fig. 1. |
View Article: PubMed Central - PubMed
Affiliation: Department of Computing, Imperial College London SW7 2AZ, UK.