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Open Access Research article

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

Wangshu Zhang1, Yong Chen1, Fengzhu Sun12 and Rui Jiang1*

Author Affiliations

1 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China

2 Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA90089, USA

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BMC Systems Biology 2011, 5:55  doi:10.1186/1752-0509-5-55

Published: 19 April 2011

Additional files

Additional file 1:

Supplemental Figures. Supplemental Figure S1 shows the results of a series of permutation test using the large domain-domain interaction network. Supplemental Figure S2 shows the mean ranks for domains with different frequency of occurrence in human proteins.

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Additional file 2:

Supplemental Tables. Supplemental Table S1 lists contributions of seed domain-disease associations (leave-one-out cross-validation experiments using the large domain-domain interaction network). Supplemental Table S2 lists contributions of seed domain-disease associations (ab initio prediction experiments using the small domain-domain interaction network). Supplemental Table S3 lists contributions of seed domain-disease associations (ab initio prediction experiments using the large domain-domain interaction network). Supplemental Table S4 lists the genome-wide evidence of associations between domains and type 1 diabetes. Supplemental Table S5 lists the genome-wide evidence of associations between domains and type 2 diabetes. Supplemental Table S6 lists the genome-wide evidence of associations between domains and Crohn's disease. Supplemental Table S7 lists the genome-wide evidence of associations between domains and breast cancer. Supplemental Table S8 lists contributions of seed domain-disease associations in the analysis of the four disease examples.

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