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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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Histograms of rank ratios for domains known to be associated with diseases. (A) Results for shortest path with Gaussian kernel, against random controls. (B) Results for shortest path with Gaussian kernel, against linkage intervals. (C) Results for shortest path with Gaussian kernel, against genome-wide scan. (D) Results for diffusion kernel, against random controls. (E) Results for diffusion kernel, against linkage intervals. (F) Results for diffusion kernels, against genome-wide scan. 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 3: Histograms of rank ratios for domains known to be associated with diseases. (A) Results for shortest path with Gaussian kernel, against random controls. (B) Results for shortest path with Gaussian kernel, against linkage intervals. (C) Results for shortest path with Gaussian kernel, against genome-wide scan. (D) Results for diffusion kernel, against random controls. (E) Results for diffusion kernel, against linkage intervals. (F) Results for diffusion kernels, against genome-wide scan. 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: For each of the three validation experiments, using either the diffusion kernel or the shortest path with Gaussian kernel, we draw a histogram of rank ratios for the entire 1,066 known associations, as shown in Figure 3. From the figure we see that rank ratios are concentrated mostly within the interval of the first few bins, and as the rank ratios increase, corresponding frequencies all take a general trend of declination. In other words, the proposed approach is capable of ranking domains known as associated with some disease phenotypes among the top of the candidates.


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)

Histograms of rank ratios for domains known to be associated with diseases. (A) Results for shortest path with Gaussian kernel, against random controls. (B) Results for shortest path with Gaussian kernel, against linkage intervals. (C) Results for shortest path with Gaussian kernel, against genome-wide scan. (D) Results for diffusion kernel, against random controls. (E) Results for diffusion kernel, against linkage intervals. (F) Results for diffusion kernels, against genome-wide scan. 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 3: Histograms of rank ratios for domains known to be associated with diseases. (A) Results for shortest path with Gaussian kernel, against random controls. (B) Results for shortest path with Gaussian kernel, against linkage intervals. (C) Results for shortest path with Gaussian kernel, against genome-wide scan. (D) Results for diffusion kernel, against random controls. (E) Results for diffusion kernel, against linkage intervals. (F) Results for diffusion kernels, against genome-wide scan. 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: For each of the three validation experiments, using either the diffusion kernel or the shortest path with Gaussian kernel, we draw a histogram of rank ratios for the entire 1,066 known associations, as shown in Figure 3. From the figure we see that rank ratios are concentrated mostly within the interval of the first few bins, and as the rank ratios increase, corresponding frequencies all take a general trend of declination. In other words, the proposed approach is capable of ranking domains known as associated with some disease phenotypes among the top of the candidates.

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