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Prediction of problematic complexes from PPI networks: sparse, embedded, and small complexes.

Yong CH, Wong L - Biol. Direct (2015)

Bottom Line: First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms.Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective.With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.

View Article: PubMed Central - PubMed

Affiliation: Graduate School for Integrative Sciences and Engineering, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore. yongchernhan@gmail.com.

ABSTRACT

Background: The prediction of protein complexes from high-throughput protein-protein interaction (PPI) data remains an important challenge in bioinformatics. Three groups of complexes have been identified as problematic to discover. First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms. Second, many complexes are embedded within highly-connected regions of the PPI network, which makes it difficult to accurately delimit their boundaries. Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective.

Results: We have previously proposed three approaches to address these challenges. First, Supervised Weighting of Composite Networks (SWC) integrates diverse data sources with supervised weighting, and successfully fills in missing co-complex edges in sparse complexes to allow them to be predicted. Second, network decomposition (DECOMP) splits the PPI network into spatially- and temporally-coherent subnetworks, allowing complexes embedded within highly-connected regions to be more clearly demarcated. Finally, Size-Specific Supervised Weighting (SSS) integrates diverse data sources with supervised learning to weight edges in a size-specific manner-of being in a small complex versus a large complex-and improves the prediction of small complexes. Here we integrate these three approaches into a single system. We test the integrated approach on the prediction of yeast and human complexes, and show that it outperforms SWC, DECOMP, or SSS when run individually, achieving the highest precision and recall levels.

Conclusion: Three groups of protein complexes remain challenging to predict from PPI data: sparse complexes, embedded complexes, and small complexes. Our previous approaches have addressed each of these challenges individually, through data integration, PPI-network decomposition, and supervised learning. Here we integrate these approaches into a single complex-discovery system, which improves the prediction of all three types of challenging complexes. With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.

No MeSH data available.


Precision-recall graphs for complex prediction in human. For clarity, a shows only the integrated approach (SWC+DECOMP+SSS), each of its constituent approaches, and the PPI+COMBINE approach, while b includes the individual clustering algorithms. c and d show the performance when the generated small clusters are removed, which lowers recall but increases precision. For human we use a matching threshold of lg_match=0.5 for large complexes, and sm_match=1 for small complexes
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Fig3: Precision-recall graphs for complex prediction in human. For clarity, a shows only the integrated approach (SWC+DECOMP+SSS), each of its constituent approaches, and the PPI+COMBINE approach, while b includes the individual clustering algorithms. c and d show the performance when the generated small clusters are removed, which lowers recall but increases precision. For human we use a matching threshold of lg_match=0.5 for large complexes, and sm_match=1 for small complexes

Mentions: Figure 3 shows the corresponding precision-recall graphs for complex prediction in human. Figure 3a shows that SWC and DECOMP both attain higher precision than PPI+COMBINE, showing the benefits of supervised weighting and PPI decomposition. SSS shows poor performance as it is limited to predicting small complexes, which is especially challenging in human. The integrated approach, SWC+DECOMP+SSS, is able to predict both large and small complexes, and achieves higher recall as well as precision. Figure 3b shows that most of the individual clustering algorithms (used with the PPI network) give lower precision or recall compared to PPI+COMBINE, showing the utility of combining the clusters from multiple clustering algorithms. The exception is Coach, which attains high precision as it does not generate small clusters by design, thereby cutting down on its false-positive predictions.Fig. 3


Prediction of problematic complexes from PPI networks: sparse, embedded, and small complexes.

Yong CH, Wong L - Biol. Direct (2015)

Precision-recall graphs for complex prediction in human. For clarity, a shows only the integrated approach (SWC+DECOMP+SSS), each of its constituent approaches, and the PPI+COMBINE approach, while b includes the individual clustering algorithms. c and d show the performance when the generated small clusters are removed, which lowers recall but increases precision. For human we use a matching threshold of lg_match=0.5 for large complexes, and sm_match=1 for small complexes
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4522147&req=5

Fig3: Precision-recall graphs for complex prediction in human. For clarity, a shows only the integrated approach (SWC+DECOMP+SSS), each of its constituent approaches, and the PPI+COMBINE approach, while b includes the individual clustering algorithms. c and d show the performance when the generated small clusters are removed, which lowers recall but increases precision. For human we use a matching threshold of lg_match=0.5 for large complexes, and sm_match=1 for small complexes
Mentions: Figure 3 shows the corresponding precision-recall graphs for complex prediction in human. Figure 3a shows that SWC and DECOMP both attain higher precision than PPI+COMBINE, showing the benefits of supervised weighting and PPI decomposition. SSS shows poor performance as it is limited to predicting small complexes, which is especially challenging in human. The integrated approach, SWC+DECOMP+SSS, is able to predict both large and small complexes, and achieves higher recall as well as precision. Figure 3b shows that most of the individual clustering algorithms (used with the PPI network) give lower precision or recall compared to PPI+COMBINE, showing the utility of combining the clusters from multiple clustering algorithms. The exception is Coach, which attains high precision as it does not generate small clusters by design, thereby cutting down on its false-positive predictions.Fig. 3

Bottom Line: First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms.Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective.With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.

View Article: PubMed Central - PubMed

Affiliation: Graduate School for Integrative Sciences and Engineering, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore. yongchernhan@gmail.com.

ABSTRACT

Background: The prediction of protein complexes from high-throughput protein-protein interaction (PPI) data remains an important challenge in bioinformatics. Three groups of complexes have been identified as problematic to discover. First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms. Second, many complexes are embedded within highly-connected regions of the PPI network, which makes it difficult to accurately delimit their boundaries. Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective.

Results: We have previously proposed three approaches to address these challenges. First, Supervised Weighting of Composite Networks (SWC) integrates diverse data sources with supervised weighting, and successfully fills in missing co-complex edges in sparse complexes to allow them to be predicted. Second, network decomposition (DECOMP) splits the PPI network into spatially- and temporally-coherent subnetworks, allowing complexes embedded within highly-connected regions to be more clearly demarcated. Finally, Size-Specific Supervised Weighting (SSS) integrates diverse data sources with supervised learning to weight edges in a size-specific manner-of being in a small complex versus a large complex-and improves the prediction of small complexes. Here we integrate these three approaches into a single system. We test the integrated approach on the prediction of yeast and human complexes, and show that it outperforms SWC, DECOMP, or SSS when run individually, achieving the highest precision and recall levels.

Conclusion: Three groups of protein complexes remain challenging to predict from PPI data: sparse complexes, embedded complexes, and small complexes. Our previous approaches have addressed each of these challenges individually, through data integration, PPI-network decomposition, and supervised learning. Here we integrate these approaches into a single complex-discovery system, which improves the prediction of all three types of challenging complexes. With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.

No MeSH data available.