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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.

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(a) The fraction of GO terms in each of CC, BP and MF obtained from entries of reconstructed gene–GO term relationship matrix obtained with and without GDV similarities (denoted in red and yellow colors, respectively). (b) Distribution of correlations of GI profiles among predicted genes associated to GO terms plotted against distributions of randomly selected gene pairs. Value of correlation, presented here, is shifted in the positive range: [0,2]
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btu470-F3: (a) The fraction of GO terms in each of CC, BP and MF obtained from entries of reconstructed gene–GO term relationship matrix obtained with and without GDV similarities (denoted in red and yellow colors, respectively). (b) Distribution of correlations of GI profiles among predicted genes associated to GO terms plotted against distributions of randomly selected gene pairs. Value of correlation, presented here, is shifted in the positive range: [0,2]

Mentions: We compute the percentage of reproduced, high confidence GO terms for CC, BP and MF separately. The results are shown in Figure 3a. Better results are achieved when GDV similarity matrices are included in the prediction model. Specifically, we capture 41% of BP terms, 41% of MF terms and 48% of CC terms. The BP and MF results outperform those of Dutkowski et al. (2013), whereas they achieve a higher percentage of reproduced GO terms in CC.Fig. 3.


Integration of molecular network data reconstructs Gene Ontology.

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

(a) The fraction of GO terms in each of CC, BP and MF obtained from entries of reconstructed gene–GO term relationship matrix obtained with and without GDV similarities (denoted in red and yellow colors, respectively). (b) Distribution of correlations of GI profiles among predicted genes associated to GO terms plotted against distributions of randomly selected gene pairs. Value of correlation, presented here, is shifted in the positive range: [0,2]
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230235&req=5

btu470-F3: (a) The fraction of GO terms in each of CC, BP and MF obtained from entries of reconstructed gene–GO term relationship matrix obtained with and without GDV similarities (denoted in red and yellow colors, respectively). (b) Distribution of correlations of GI profiles among predicted genes associated to GO terms plotted against distributions of randomly selected gene pairs. Value of correlation, presented here, is shifted in the positive range: [0,2]
Mentions: We compute the percentage of reproduced, high confidence GO terms for CC, BP and MF separately. The results are shown in Figure 3a. Better results are achieved when GDV similarity matrices are included in the prediction model. Specifically, we capture 41% of BP terms, 41% of MF terms and 48% of CC terms. The BP and MF results outperform those of Dutkowski et al. (2013), whereas they achieve a higher percentage of reproduced GO terms in CC.Fig. 3.

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