Research article
An integrated approach to the prediction of domain-domain interactions
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* Corresponding authors: Fengzhu Sun fsun@usc.edu - Ting Chen tingchen@usc.edu
1 Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
2 School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, P.R. China
3 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089-2910, USA
BMC Bioinformatics 2006, 7:269 doi:10.1186/1471-2105-7-269
Published: 25 May 2006Additional files
Additional file 1:
False positive (fp) and false negative (fn) of the observed protein interactions. It contains equations to calculate fp and fn values for the protein interactions used in the study and effects of various fp and fn values to the inference the domain interactions.
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Additional file 2:
The likelihood ratio of six data sources. The values for domain interactions inferred from six data sources are binned into discrete intervals and the likelihood ratio is calculated.
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Additional file 3:
Comparison with predicted conserved domain interactions and random interactions. Table S2 shows the significance of the number of predicted conserved domain interactions compared to the random interactions.
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Additional file 4:
List of conserved domain interactions predicted from protein interactions of at least three species. These conserved domain interaction have 31% of overlaps with domain interactions in iPfam.
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Additional file 5:
ROC curves of predicted domain interactions using yeast, worm, fruitfly and humans. Figure S2 shows the comparison of performances of score functions to predict domain interactions for four species.
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Additional file 6:
The 2,391 high-confidence domain interactions from the Bayesian approach. Domain pairs are sorted by the rank based on the Bayesian approach. Rankings by evidence counting (EV) and Logistic Regression (LR) are presented with the number of evidences and the probability by LR.
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Additional file 7:
The likelihood ratio of all domain interactions. Domain pairs with larger than zero likelihood ratio are sorted by the rank based on the Bayesian approach. Rankings by evidence counting (EV) and Logistic Regression (LR) are presented with the number of evidences and the probability by LR.
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Additional file 8:
The likelihood ratio values of predicted domain interaction, the numbers of predicted domain interactions, and the overlap with domain interactions from H. pylori. We used 1,101 domain interactions in H. pylori involving 206 domains. Numbers in the first column indicate the likelihood ratio values for the domain interactions, and the second column is the number of interactions having the corresponding likelihood ratio values. "Fold" indicates the ratio of the fraction over expected value. (5.2%).
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Additional file 9:
A ROC curve of predicted domain interactions using H. pylori. Figure S3 shows the comparison of performances of score functions to predict domain interactions for H. pylori.
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