2017
DOI: 10.1093/bib/bbx090
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Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

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… Show more

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Cited by 12 publications
(8 citation statements)
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“…The time-specific directed network at a time point is constructed based on an information-theoretic scheme ( Sun et al , 2019 ), which provides a direction determination index (as defined in Eq. 3 ) to evaluate the combined effect of gene combinations over a single gene from the perspective of mutual information (MI).…”
Section: Methodsmentioning
confidence: 99%
“…The time-specific directed network at a time point is constructed based on an information-theoretic scheme ( Sun et al , 2019 ), which provides a direction determination index (as defined in Eq. 3 ) to evaluate the combined effect of gene combinations over a single gene from the perspective of mutual information (MI).…”
Section: Methodsmentioning
confidence: 99%
“…Given the difficulty in finding genes that regulate the reprogramming of glucose metabolism from a large number of genes, multi-omics integration analysis helps to define key genes that lead to metabolic reprogramming in PCa. To identify the important biomolecules, several computational methods based on network analysis have been proposed [ 6 , 7 , 8 ]. The differential network (DN) analysis method aims at identifying the disease-related important biomolecules or modules [ 9 ].…”
Section: Introductionmentioning
confidence: 99%
“…to identify relationships between biomolecules, reduce false associations, and guide biological network construction. Combining gene expression data and protein–protein interaction (PPI) networks to define key nodes of biological development (MarkRank) has shown good predictive accuracy and high specificity associated with the disease [ 8 ]. K-DN can also cover experimental data gaps to explore the key factors affecting disease.…”
Section: Introductionmentioning
confidence: 99%
“…This new paradigm reflects the fact that human diseases are not caused by single molecular defects but are driven by complex interactions among a variety of molecular mediators [15]. The network-based methods have developed a plethora of topological parameters for discovering biomarkers [16], disease-associated genes [17], and drug targets [18,19]. Hub genes with higher node degree in PPI networks have been predicted to be diagnostic biomarkers for NSCLC and some have been experimentally validated, such as NCAPH [4].…”
Section: Introductionmentioning
confidence: 99%