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DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases.

Zhang W, Chen Y, Sun F, Jiang R - BMC Syst Biol (2011)

Bottom Line: Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases.The proposed approach effectively ranks susceptible domains among the top of the candidates, and it is robust to the parameters involved.The predicted landscape provides a comprehensive understanding of associations between domains and human diseases.

View Article: PubMed Central - HTML - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China.

ABSTRACT

Background: Domains are basic units of proteins, and thus exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention, diagnosis and treatment of these diseases. Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases. Based on this assumption, we propose a Bayesian regression approach named "domainRBF" (domain Rank with Bayes Factor) to prioritize candidate domains for human complex diseases.

Results: Using a compiled dataset containing 1,614 associations between 671 domains and 1,145 disease phenotypes, we demonstrate the effectiveness of the proposed approach through three large-scale leave-one-out cross-validation experiments (random control, simulated linkage interval, and genome-wide scan), and we do so in terms of three criteria (precision, mean rank ratio, and AUC score). We further show that the proposed approach is robust to the parameters involved and the underlying domain-domain interaction network through a series of permutation tests. Once having assessed the validity of this approach, we show the possibility of ab initio inference of domain-disease associations and gene-disease associations, and we illustrate the strong agreement between our inferences and the evidences from genome-wide association studies for four common diseases (type 1 diabetes, type 2 diabetes, Crohn's disease, and breast cancer). Finally, we provide a pre-calculated genome-wide landscape of associations between 5,490 protein domains and 5,080 human diseases and offer free access to this resource.

Conclusions: The proposed approach effectively ranks susceptible domains among the top of the candidates, and it is robust to the parameters involved. The ab initio inference of domain-disease associations shows strong agreement with the evidence provided by genome-wide association studies. The predicted landscape provides a comprehensive understanding of associations between domains and human diseases.

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ROC curves of the leave-one-out cross-validation experiments. (A) Results for random controls. (B) Results for linkage intervals. (C) Results for genome-wide scan. BF: the domainRBF approach (using Bayes factors as scores for candidate domains). R2: the ordinary non-Bayesian linear regression approach (using R-square as scores for candidate domains). SG: shortest path with Gaussian kernel. DK: diffusion kernel. Numbers in the parentheses are AUC scores of the corresponding ROC curves. The small domain-domain interaction network composed of the PDB part of the DOMINE database is used to obtain the results.
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Figure 4: ROC curves of the leave-one-out cross-validation experiments. (A) Results for random controls. (B) Results for linkage intervals. (C) Results for genome-wide scan. BF: the domainRBF approach (using Bayes factors as scores for candidate domains). R2: the ordinary non-Bayesian linear regression approach (using R-square as scores for candidate domains). SG: shortest path with Gaussian kernel. DK: diffusion kernel. Numbers in the parentheses are AUC scores of the corresponding ROC curves. The small domain-domain interaction network composed of the PDB part of the DOMINE database is used to obtain the results.

Mentions: Third, we conjecture from these results that the domainRBF approach with some proper defined priors can achieve higher performance than the non-Bayesian linear regression method. We compare the performance of the (Bayesian) domainRBF approach with the (non-Bayesian) ordinary linear regression method through the three large-scale leave-one-out cross-validation experiments, and we also list the results in Table 1. Although both approaches can successfully recover the associations between protein domains and human disease phenotypes, the results show that the domainRBF approach can achieve better performance than the ordinary linear regression approach in most cases. For example, in all three cross-validation experiments, the domainRBF approach can achieve higher precisions (with only two exceptions for genome-wide scan), smaller mean rank ratios (for at least 5.32%), and larger AUC scores (for at least 3.19%). When looking at the ROC curves (Figure 4), we see that the curve of the domainRBF approach climbs much faster towards the upper left corner of the plot than does that of the ordinary linear regression approach, suggesting that the Bayesian domainRBF approach is superior to the non-Bayesian ordinary linear regression method.


DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases.

Zhang W, Chen Y, Sun F, Jiang R - BMC Syst Biol (2011)

ROC curves of the leave-one-out cross-validation experiments. (A) Results for random controls. (B) Results for linkage intervals. (C) Results for genome-wide scan. BF: the domainRBF approach (using Bayes factors as scores for candidate domains). R2: the ordinary non-Bayesian linear regression approach (using R-square as scores for candidate domains). SG: shortest path with Gaussian kernel. DK: diffusion kernel. Numbers in the parentheses are AUC scores of the corresponding ROC curves. The small domain-domain interaction network composed of the PDB part of the DOMINE database is used to obtain the results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108930&req=5

Figure 4: ROC curves of the leave-one-out cross-validation experiments. (A) Results for random controls. (B) Results for linkage intervals. (C) Results for genome-wide scan. BF: the domainRBF approach (using Bayes factors as scores for candidate domains). R2: the ordinary non-Bayesian linear regression approach (using R-square as scores for candidate domains). SG: shortest path with Gaussian kernel. DK: diffusion kernel. Numbers in the parentheses are AUC scores of the corresponding ROC curves. The small domain-domain interaction network composed of the PDB part of the DOMINE database is used to obtain the results.
Mentions: Third, we conjecture from these results that the domainRBF approach with some proper defined priors can achieve higher performance than the non-Bayesian linear regression method. We compare the performance of the (Bayesian) domainRBF approach with the (non-Bayesian) ordinary linear regression method through the three large-scale leave-one-out cross-validation experiments, and we also list the results in Table 1. Although both approaches can successfully recover the associations between protein domains and human disease phenotypes, the results show that the domainRBF approach can achieve better performance than the ordinary linear regression approach in most cases. For example, in all three cross-validation experiments, the domainRBF approach can achieve higher precisions (with only two exceptions for genome-wide scan), smaller mean rank ratios (for at least 5.32%), and larger AUC scores (for at least 3.19%). When looking at the ROC curves (Figure 4), we see that the curve of the domainRBF approach climbs much faster towards the upper left corner of the plot than does that of the ordinary linear regression approach, suggesting that the Bayesian domainRBF approach is superior to the non-Bayesian ordinary linear regression method.

Bottom Line: Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases.The proposed approach effectively ranks susceptible domains among the top of the candidates, and it is robust to the parameters involved.The predicted landscape provides a comprehensive understanding of associations between domains and human diseases.

View Article: PubMed Central - HTML - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China.

ABSTRACT

Background: Domains are basic units of proteins, and thus exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention, diagnosis and treatment of these diseases. Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases. Based on this assumption, we propose a Bayesian regression approach named "domainRBF" (domain Rank with Bayes Factor) to prioritize candidate domains for human complex diseases.

Results: Using a compiled dataset containing 1,614 associations between 671 domains and 1,145 disease phenotypes, we demonstrate the effectiveness of the proposed approach through three large-scale leave-one-out cross-validation experiments (random control, simulated linkage interval, and genome-wide scan), and we do so in terms of three criteria (precision, mean rank ratio, and AUC score). We further show that the proposed approach is robust to the parameters involved and the underlying domain-domain interaction network through a series of permutation tests. Once having assessed the validity of this approach, we show the possibility of ab initio inference of domain-disease associations and gene-disease associations, and we illustrate the strong agreement between our inferences and the evidences from genome-wide association studies for four common diseases (type 1 diabetes, type 2 diabetes, Crohn's disease, and breast cancer). Finally, we provide a pre-calculated genome-wide landscape of associations between 5,490 protein domains and 5,080 human diseases and offer free access to this resource.

Conclusions: The proposed approach effectively ranks susceptible domains among the top of the candidates, and it is robust to the parameters involved. The ab initio inference of domain-disease associations shows strong agreement with the evidence provided by genome-wide association studies. The predicted landscape provides a comprehensive understanding of associations between domains and human diseases.

Show MeSH
Related in: MedlinePlus