Schematic representation of datasets used in this study

Integration of molecular network data reconstructs Gene Ontology. Gligorijević V, Janjić V, Pržulj N - Bioinformatics (2014) Bottom Line: 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. View Article: PubMed Central - PubMed Affiliation: Department of Computing, Imperial College London SW7 2AZ, UK. Show MeSH Major Gene Ontology* Gene Regulatory Networks* Protein Interaction Mapping* Minor Algorithms Cluster Analysis Computational Biology/methods Gene Expression Molecular Sequence Annotation Saccharomyces cerevisiae/genetics/metabolism	© Copyright Policy - creative-commons Related In: Results - Collection License Show All Figures getmorefigures.php?uid=PMC4230235&req=5 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.

Integration of molecular network data reconstructs Gene Ontology.

Gligorijević V, Janjić V, Pržulj N - Bioinformatics (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing, Imperial College London SW7 2AZ, UK.

Show MeSH

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

Related In: Results - Collection

License

Show All Figures

getmorefigures.php?uid=PMC4230235&req=5

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.