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MarkRank: Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

• Last updated: January 16, 2017

Introduction#

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. In this study, we proposed a novel method, MarkRank, to discover cooperative biomarkers for heterogeneous diseases by integrating multi-source information, including biological networks, prior information about related diseases, and the cooperative effects of gene combinations. By innovatively constructing a gene cooperation network to capture the cooperative effects of gene combinations and explicitly taking the heterogeneity of complex diseases into consideration, MarkRank achieves superior performance compared to existing methods in both simulation studies and real datasets. The biomarkers identified by MarkRank not only have a better prediction accuracy in the Monte Carlo cross-validation procedure but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, the MarkRank method is an efficient and effective computational tool for discovering disease biomarkers and can help in the exploration of the underlying pathogenesis of complex diseases. MarkRank has been implemented in the R package Corbi, which is publicly available at the CRAN website (http://cran.r-project.org/web/packages/Corbi/). The open source codes, original datasets and additional materials are available at http://doc.aporc.org/wiki/MarkRank.

Reference#

• Duanchen Sun, Xianwen Ren, Eszter Ari, Tamas Korcsmaros, Peter Csermely, Ling-Yun Wu. Discovering cooperative biomarkers for heterogeneous complex disease diagnoses. In submission, 2017.

Software#

R package#

The MarkRank method has been implemented as function markrank in R package Corbi, which can be found at:

• Corbi

Additional Materials#

• Supplementary Materials.pdf
• Enrichment_pathway_description.xlsx
• MarkRank_gene_summary.xlsx
• All_methods_gene_summary.xlsx
• BiNGO results.zip
• MarkRank code and data.zip

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Category: Supplementary Software