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Integrating diverse biological and computational sources for reliable protein-protein interactions.

Wu M, Li X, Chua HN, Kwoh CK, Ng SK - BMC Bioinformatics (2010)

Bottom Line: We performed comprehensive experiments on two benchmark yeast PPI datasets.The experimental results showed that our proposed method can effectively eliminate false positives in detected PPIs and identify false negatives by predicting novel yet reliable PPIs.Our proposed method also performed significantly better than merely using each of individual evidence sources, illustrating the importance of integrating various biological and computational sources of data and evidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg

ABSTRACT

Background: Protein-protein interactions (PPIs) play important roles in various cellular processes. However, the low quality of current PPI data detected from high-throughput screening techniques has diminished the potential usefulness of the data. We need to develop a method to address the high data noise and incompleteness of PPI data, namely, to filter out inaccurate protein interactions (false positives) and predict putative protein interactions (false negatives).

Results: In this paper, we proposed a novel two-step method to integrate diverse biological and computational sources of supporting evidence for reliable PPIs. The first step, interaction binning or InterBIN, groups PPIs together to more accurately estimate the likelihood (Bin-Confidence score) that the protein pairs interact for each biological or computational evidence source. The second step, interaction classification or InterCLASS, integrates the collected Bin-Confidence scores to build classifiers and identify reliable interactions.

Conclusions: We performed comprehensive experiments on two benchmark yeast PPI datasets. The experimental results showed that our proposed method can effectively eliminate false positives in detected PPIs and identify false negatives by predicting novel yet reliable PPIs. Our proposed method also performed significantly better than merely using each of individual evidence sources, illustrating the importance of integrating various biological and computational sources of data and evidence.

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AUC for each method as the parameter μ varies on DIP data. Figure 1 shows the AUC for each method as the parameter μ varies on DIP data.
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Figure 1: AUC for each method as the parameter μ varies on DIP data. Figure 1 shows the AUC for each method as the parameter μ varies on DIP data.

Mentions: The parameter μ, which was used to determine the group size, has a direct effect on the Bin-Confidences and on the accuracy for methods using Bin-Confidences. Figure 1 and 2 show the AUC for each method on DIP data and BioGrid data respectively as μ varies.


Integrating diverse biological and computational sources for reliable protein-protein interactions.

Wu M, Li X, Chua HN, Kwoh CK, Ng SK - BMC Bioinformatics (2010)

AUC for each method as the parameter μ varies on DIP data. Figure 1 shows the AUC for each method as the parameter μ varies on DIP data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957691&req=5

Figure 1: AUC for each method as the parameter μ varies on DIP data. Figure 1 shows the AUC for each method as the parameter μ varies on DIP data.
Mentions: The parameter μ, which was used to determine the group size, has a direct effect on the Bin-Confidences and on the accuracy for methods using Bin-Confidences. Figure 1 and 2 show the AUC for each method on DIP data and BioGrid data respectively as μ varies.

Bottom Line: We performed comprehensive experiments on two benchmark yeast PPI datasets.The experimental results showed that our proposed method can effectively eliminate false positives in detected PPIs and identify false negatives by predicting novel yet reliable PPIs.Our proposed method also performed significantly better than merely using each of individual evidence sources, illustrating the importance of integrating various biological and computational sources of data and evidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg

ABSTRACT

Background: Protein-protein interactions (PPIs) play important roles in various cellular processes. However, the low quality of current PPI data detected from high-throughput screening techniques has diminished the potential usefulness of the data. We need to develop a method to address the high data noise and incompleteness of PPI data, namely, to filter out inaccurate protein interactions (false positives) and predict putative protein interactions (false negatives).

Results: In this paper, we proposed a novel two-step method to integrate diverse biological and computational sources of supporting evidence for reliable PPIs. The first step, interaction binning or InterBIN, groups PPIs together to more accurately estimate the likelihood (Bin-Confidence score) that the protein pairs interact for each biological or computational evidence source. The second step, interaction classification or InterCLASS, integrates the collected Bin-Confidence scores to build classifiers and identify reliable interactions.

Conclusions: We performed comprehensive experiments on two benchmark yeast PPI datasets. The experimental results showed that our proposed method can effectively eliminate false positives in detected PPIs and identify false negatives by predicting novel yet reliable PPIs. Our proposed method also performed significantly better than merely using each of individual evidence sources, illustrating the importance of integrating various biological and computational sources of data and evidence.

Show MeSH