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InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes.

Sun J, Sun Y, Ding G, Liu Q, Wang C, He Y, Shi T, Li Y, Zhao Z - BMC Bioinformatics (2007)

Bottom Line: After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration.So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison.It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

View Article: PubMed Central - HTML - PubMed

Affiliation: Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA. jsun@vcu.edu

ABSTRACT

Background: Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

Results: In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface.

Conclusion: This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli.

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PPI prediction by InPrePPI with different k values.
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Figure 3: PPI prediction by InPrePPI with different k values.

Mentions: The results in the above two sections indicate that each method has its own superiority and no one outperforms the others. Thus, we developed a new method, InPrePPI, which weighs the genomic context information utilized in these four methods and integrates it into a system that can optimize the prediction. Specifically, the InPrePPI uses the AC values of the four methods based on three positive datasets (KEGG, EcoCyc, and DIP). A constant, k, is used in the integration process (see Methods). This k can be obtained by a heuristic approach. We tested k values from 0 to 1 (in an interval 0.1) and from 1 to 30 (in an interval 1). For each k, we calculated the integrated score () for each protein pair and then obtained a set of PPIs with the highest scores (InPrePPI_high, see Methods). The optimal k value is found when it results in the highest AC value in the InPrePPI_high class. Figure 3 shows the AC values using different k values and the InPrePPI_high class. The AC values increased when k increased until k reached 15. Thus, the optimal k was set to 15.


InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes.

Sun J, Sun Y, Ding G, Liu Q, Wang C, He Y, Shi T, Li Y, Zhao Z - BMC Bioinformatics (2007)

PPI prediction by InPrePPI with different k values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238723&req=5

Figure 3: PPI prediction by InPrePPI with different k values.
Mentions: The results in the above two sections indicate that each method has its own superiority and no one outperforms the others. Thus, we developed a new method, InPrePPI, which weighs the genomic context information utilized in these four methods and integrates it into a system that can optimize the prediction. Specifically, the InPrePPI uses the AC values of the four methods based on three positive datasets (KEGG, EcoCyc, and DIP). A constant, k, is used in the integration process (see Methods). This k can be obtained by a heuristic approach. We tested k values from 0 to 1 (in an interval 0.1) and from 1 to 30 (in an interval 1). For each k, we calculated the integrated score () for each protein pair and then obtained a set of PPIs with the highest scores (InPrePPI_high, see Methods). The optimal k value is found when it results in the highest AC value in the InPrePPI_high class. Figure 3 shows the AC values using different k values and the InPrePPI_high class. The AC values increased when k increased until k reached 15. Thus, the optimal k was set to 15.

Bottom Line: After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration.So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison.It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

View Article: PubMed Central - HTML - PubMed

Affiliation: Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA. jsun@vcu.edu

ABSTRACT

Background: Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

Results: In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface.

Conclusion: This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli.

Show MeSH
Related in: MedlinePlus