Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

Duanchen Sun, Xianwen Ren, Eszter Ari, T. Korcsmáros, P. Csermely, Ling Yun Wu

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)89-101
Number of pages13
JournalBriefings in Bioinformatics
Volume20
Issue number1
DOIs
Publication statusPublished - Jan 18 2019

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Biomarkers
Genes
Gene Regulatory Networks
Biological Phenomena
Transcriptome
Gene expression

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

Cite this

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses. / Sun, Duanchen; Ren, Xianwen; Ari, Eszter; Korcsmáros, T.; Csermely, P.; Wu, Ling Yun.

In: Briefings in Bioinformatics, Vol. 20, No. 1, 18.01.2019, p. 89-101.

Research output: Contribution to journalArticle

Sun, Duanchen ; Ren, Xianwen ; Ari, Eszter ; Korcsmáros, T. ; Csermely, P. ; Wu, Ling Yun. / Discovering cooperative biomarkers for heterogeneous complex disease diagnoses. In: Briefings in Bioinformatics. 2019 ; Vol. 20, No. 1. pp. 89-101.
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