Kivonat:
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.
