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Brief Bioinform. 2019 Jan 18;20(1):89-101. doi: 10.1093/bib/bbx090.

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses.

Author information

1
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
2
Biodynamic Optical Imaging Center, Peking University, Beijing, China.
3
Department of Genetics, Eötvös Loránd University, Budapest.
4
Institute of Food Research and the Earlham Institute, Norwich, UK.
5
Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.

Abstract

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. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy 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, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

PMID:
28968712
DOI:
10.1093/bib/bbx090

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