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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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Scheme of inferring gene-disease associations from domain-disease associations.
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Figure 7: Scheme of inferring gene-disease associations from domain-disease associations.

Mentions: Inspired by the success of ab initio inference of domains and diseases, we further propose the following application of the proposed domainRBF approach in the inference of genes that are associated with diseases, by combining predicted domain-disease associations and known domain-protein relations. As shown in Figure 7, given a query disease and a gene whose products (proteins) are usually composed of several protein domains, we look at corresponding Bayes factors of these domains and define an association score that measures the strength of association between the gene and the disease as the maximum among these Bayes factors. Then, given a set of candidate genes, we are able to obtain association scores for the genes and further rank the genes according to their scores.


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)

Scheme of inferring gene-disease associations from domain-disease associations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Scheme of inferring gene-disease associations from domain-disease associations.
Mentions: Inspired by the success of ab initio inference of domains and diseases, we further propose the following application of the proposed domainRBF approach in the inference of genes that are associated with diseases, by combining predicted domain-disease associations and known domain-protein relations. As shown in Figure 7, given a query disease and a gene whose products (proteins) are usually composed of several protein domains, we look at corresponding Bayes factors of these domains and define an association score that measures the strength of association between the gene and the disease as the maximum among these Bayes factors. Then, given a set of candidate genes, we are able to obtain association scores for the genes and further rank the genes according to their scores.

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