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MarkRank: Prioritization of network biomarkers for complex diseases

• Last updated: July 22, 2016

Introduction#

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases, which can further facilitate disease diagnosis and therapy. With the powerful technologies stemming from systems biology, the types of disease-related biomarkers have been gradually refocused from conventional individual genes to current network-based disease biomarker modules. Techniques integrating both expression profiles and biological networks for the identification of connected disease biomarkers are receiving increasing interest. However, the incompleteness of protein-protein interaction (PPI) networks and the intrinsic heterogeneity of complex diseases may hinder the efficacy of connected biomarker identification. In this paper, we propose a novel method, MarkRank, to prioritize disease genes by integrating multi-source information including human PPI network, prior information about related diseases, and the discriminative power of cooperative gene combinations. In comparison with well-designed simulation studies and real biological datasets, MarkRank, explicitly taking the heterogeneity of diseases into consideration, achieves a superior performance than existing methods, exhibits high specificity associated with the related diseases, and can help exploring the underlying pathogenesis of complex disease in a new perspective. MarkRank has been implemented in the R package Corbi, which can be readily installed and used in R.

Reference#

• Duanchen Sun, Xianwen Ren, Eszter Ari, Tamas Korcsmaros, Peter Csermely, Ling-Yun Wu. Prioritization of network biomarkers for complex diseases via MarkRank. In submission, 2016.

Software#

R package#

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

• Corbi

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Category: Supplementary Software