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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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Related in: MedlinePlus

Bayes factors of the original and permuted data. "original", "permuted PPS", "permuted seed", "random seed", and "permuted DDI" denote the results obtained using the original data, permuted phenotype similarity profile, permuted domain-disease associations, randomly selected seed domains, and permuted domain-domain interaction network, respectively. The small domain-domain interaction network composed of the PDB part of the DOMINE database and the diffusion kernel are used to obtain the results.
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Figure 2: Bayes factors of the original and permuted data. "original", "permuted PPS", "permuted seed", "random seed", and "permuted DDI" denote the results obtained using the original data, permuted phenotype similarity profile, permuted domain-disease associations, randomly selected seed domains, and permuted domain-domain interaction network, respectively. The small domain-domain interaction network composed of the PDB part of the DOMINE database and the diffusion kernel are used to obtain the results.

Mentions: To further substantiate this point, we perform a series of permutations towards disease-disease, domain-disease, and domain-domain relationships. First, we break the disease-disease relationship by permuting the phenotype similarity profile. Second, we break the domain-disease relationship by two methods: (1) permuting domain-disease associations and (2) replacing domains in known disease-domain associations with randomly selected domains. Third, we break the domain-domain relationship by permuting connections in the underlying domain-domain interaction network, while keeping node degrees and recalculating the diffusion kernel. For each of the above permutations, we calculate Bayes factors of disease domains and present the results in Figure 2, which shows that the median of Bayes factors based on the original data is much higher than the medians obtained from the different permuted relationships, as described above.


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)

Bayes factors of the original and permuted data. "original", "permuted PPS", "permuted seed", "random seed", and "permuted DDI" denote the results obtained using the original data, permuted phenotype similarity profile, permuted domain-disease associations, randomly selected seed domains, and permuted domain-domain interaction network, respectively. The small domain-domain interaction network composed of the PDB part of the DOMINE database and the diffusion kernel are used to obtain the results.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Bayes factors of the original and permuted data. "original", "permuted PPS", "permuted seed", "random seed", and "permuted DDI" denote the results obtained using the original data, permuted phenotype similarity profile, permuted domain-disease associations, randomly selected seed domains, and permuted domain-domain interaction network, respectively. The small domain-domain interaction network composed of the PDB part of the DOMINE database and the diffusion kernel are used to obtain the results.
Mentions: To further substantiate this point, we perform a series of permutations towards disease-disease, domain-disease, and domain-domain relationships. First, we break the disease-disease relationship by permuting the phenotype similarity profile. Second, we break the domain-disease relationship by two methods: (1) permuting domain-disease associations and (2) replacing domains in known disease-domain associations with randomly selected domains. Third, we break the domain-domain relationship by permuting connections in the underlying domain-domain interaction network, while keeping node degrees and recalculating the diffusion kernel. For each of the above permutations, we calculate Bayes factors of disease domains and present the results in Figure 2, which shows that the median of Bayes factors based on the original data is much higher than the medians obtained from the different permuted relationships, as described above.

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