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Discovery of small protein complexes from PPI networks with size-specific supervised weighting.

Yong CH, Maruyama O, Wong L - BMC Syst Biol (2014)

Bottom Line: SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex.We test our approach on the prediction of yeast and human small complexes, and demonstrate that our approach attains higher precision and recall than some popular complex prediction algorithms.Furthermore, our approach generates a greater number of novel predictions with higher quality in terms of functional coherence.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
The prediction of small complexes (consisting of two or three distinct proteins) is an important and challenging subtask in protein complex prediction from protein-protein interaction (PPI) networks. The prediction of small complexes is especially susceptible to noise (missing or spurious interactions) in the PPI network, while smaller groups of proteins are likelier to take on topological characteristics of real complexes by chance. We propose a two-stage approach, SSS and Extract, for discovering small complexes. First, the PPI network is weighted by size-specific supervised weighting (SSS), which integrates heterogeneous data and their topological features with an overall topological isolatedness feature. SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex. The second stage, Extract, analyzes the SSS-weighted network to extract putative small complexes and scores them by cohesiveness-weighted density, which incorporates both small-co-complex and large-co-complex weights of edges within and surrounding the complexes. We test our approach on the prediction of yeast and human small complexes, and demonstrate that our approach attains higher precision and recall than some popular complex prediction algorithms. Furthermore, our approach generates a greater number of novel predictions with higher quality in terms of functional coherence.

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Related in: MedlinePlus

Performance of small complex prediction in human, (a) precision-recall AUC, (b) and (c) precision-recall graphs.
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Figure 3: Performance of small complex prediction in human, (a) precision-recall AUC, (b) and (c) precision-recall graphs.

Mentions: Figure 3 shows the performance of prediction of human small complexes. The prediction of complexes in human is much more challenging than in yeast, so the AUCs achieved here are correspondingly lower. Nonetheless, our approach (SSS + Extract) still outperforms all the other approaches, including using the PPIREL or SSS-weighted networks with standard clustering algorithms, or the PPIREL-weighted network with Extract. When using standard clustering algorithms to discover small complexes, weighting the network with SSS gives improved performance only for CMC and IPCA, while performance remains the same or decreases for the other clustering algorithms.


Discovery of small protein complexes from PPI networks with size-specific supervised weighting.

Yong CH, Maruyama O, Wong L - BMC Syst Biol (2014)

Performance of small complex prediction in human, (a) precision-recall AUC, (b) and (c) precision-recall graphs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4305982&req=5

Figure 3: Performance of small complex prediction in human, (a) precision-recall AUC, (b) and (c) precision-recall graphs.
Mentions: Figure 3 shows the performance of prediction of human small complexes. The prediction of complexes in human is much more challenging than in yeast, so the AUCs achieved here are correspondingly lower. Nonetheless, our approach (SSS + Extract) still outperforms all the other approaches, including using the PPIREL or SSS-weighted networks with standard clustering algorithms, or the PPIREL-weighted network with Extract. When using standard clustering algorithms to discover small complexes, weighting the network with SSS gives improved performance only for CMC and IPCA, while performance remains the same or decreases for the other clustering algorithms.

Bottom Line: SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex.We test our approach on the prediction of yeast and human small complexes, and demonstrate that our approach attains higher precision and recall than some popular complex prediction algorithms.Furthermore, our approach generates a greater number of novel predictions with higher quality in terms of functional coherence.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
The prediction of small complexes (consisting of two or three distinct proteins) is an important and challenging subtask in protein complex prediction from protein-protein interaction (PPI) networks. The prediction of small complexes is especially susceptible to noise (missing or spurious interactions) in the PPI network, while smaller groups of proteins are likelier to take on topological characteristics of real complexes by chance. We propose a two-stage approach, SSS and Extract, for discovering small complexes. First, the PPI network is weighted by size-specific supervised weighting (SSS), which integrates heterogeneous data and their topological features with an overall topological isolatedness feature. SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex. The second stage, Extract, analyzes the SSS-weighted network to extract putative small complexes and scores them by cohesiveness-weighted density, which incorporates both small-co-complex and large-co-complex weights of edges within and surrounding the complexes. We test our approach on the prediction of yeast and human small complexes, and demonstrate that our approach attains higher precision and recall than some popular complex prediction algorithms. Furthermore, our approach generates a greater number of novel predictions with higher quality in terms of functional coherence.

Show MeSH
Related in: MedlinePlus