Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain interactions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.
Animals ; Caenorhabditis elegans Proteins ; chemistry ; genetics ; metabolism ; Confidence Intervals ; Databases, Protein ; Gene Expression Profiling ; statistics & numerical data ; Humans ; Likelihood Functions ; Phylogeny ; Protein Array Analysis ; methods ; statistics & numerical data ; Protein Interaction Mapping ; methods ; statistics & numerical data ; Protein Structure, Tertiary ; ROC Curve ; Saccharomyces cerevisiae Proteins ; chemistry ; genetics ; metabolism ; Sensitivity and Specificity

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.

OBJECTIVES: Enterobius vermicularis is a parasitic disease that is common in crowded areas such as schools and kindergartens. Primary investigations of electronic evidence have reported different prevalences of E. vermicularis in Iran. Therefore, we aimed to estimate the total prevalence of this infection among Iranian children using a meta-analysis. METHODS: Relevant studies were identified in national and international databases. We selected eligible papers for meta-analysis after investigating titles, abstracts, and full texts; assessing study quality; and applying inclusion/exclusion criteria. Data were extracted by two independent researchers. The results were combined using a random effects model in Stata v. 11 software. RESULTS: Among 19 eligible articles including 11,676 participants, the prevalences of E. vermicularis among all children, boys, and girls were 1.2%–66.1%, 2.3%–65.5%, and 1.7%–65.5%, respectively. Pooled prevalences (95% confidence interval) of E. vermicularis among all children, boys, and girls were 17.2% (12.6%–21.8%), 17.2% (12.6%–21.8%), and 16.9% (9.03%–24.8%), respectively. CONCLUSION: This meta-analysis showed that a great majority of Iranian children are infected with E. vermicularis, possibly due to poor public health.
Child* ; Enterobius* ; Female ; Humans ; Iran* ; Parasitic Diseases ; Prevalence* ; Public Health