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Quantitative Biology

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Quant. Biol.    2016, Vol. 4 Issue (4) : 310-319    https://doi.org/10.1007/s40484-016-0084-z
REVIEW
A survey on biomarker identification based on molecular networks
Guanghui Zhu, Xing-Ming Zhao(), Jun Wu()
Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
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Abstract

Background: Identifying biomarkers for accurate diagnosis and prognosis of diseases is important for the prevention of disease development. The molecular networks that describe the functional relationships among molecules provide a global view of the complex biological systems. With the molecular networks, the molecular mechanisms underlying diseases can be unveiled, which helps identify biomarkers in a systematic way.

Results: In this survey, we report the recent progress on identifying biomarkers based on the topology of molecular networks, and we categorize those biomarkers into three groups, including node biomarkers, edge biomarkers and network biomarkers. These distinct types of biomarkers can be detected under different conditions depending on the data available.

Conclusions: The biomarkers identified based on molecular networks can provide more accurate diagnosis and prognosis. The pros and cons of different types of biomarkers as well as future directions to improve the methods for identifying biomarkers are also discussed.
Author Summary  The biomarkers identified based on molecular networks can be of help for accurate diagnosis and prognosis in a more systematic way. In this survey, three types of network based biomarkers are introduced, including node biomarkers, edge biomarkers and network biomarkers. The computational approaches for detecting the network based biomarkers are also introduced.
Keywords biomarker      molecular network      module      pathway     
Corresponding Author(s): Xing-Ming Zhao,Jun Wu   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.
Just Accepted Date: 28 September 2016   Online First Date: 09 November 2016    Issue Date: 01 December 2016
 Cite this article:   
Guanghui Zhu,Xing-Ming Zhao,Jun Wu. A survey on biomarker identification based on molecular networks[J]. Quant. Biol., 2016, 4(4): 310-319.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-016-0084-z
https://academic.hep.com.cn/qb/EN/Y2016/V4/I4/310
Fig.1  Illustration of three types of network based biomarkers.
Database Description Interaction type Interaction quality
HumanNet A probabilistic functional gene network of Homo sapiens Interaction Computational confidence is provided
PathwayCommons Collection of biological pathway information collected from public pathway databases Pathway The pathways are either experimentally determined or computationally predicted
BIND A biomolecular interaction network database Interaction and pathway The interactions are based on experiments and literature
MicroCosm A database contains computationally predicted targets for microRNAs across various species MiRNA-target gene interaction Computational confidence is provided
InnateDB A database contains experimentally-verified interactions and signaling pathways involved in the innate immune response of humans, mice and bovines to microbial infection. Interaction and signaling pathway The interactions are experimentally verified
BioGRID Collections of physical and genetic interactions for major model organisms Interaction The interactions are curated from high-throughput datasets and individual focused studies
DIP The database contains experimentally determined interactions between proteins Interaction The interactions are experimentally determined
HPRD A database about human proteins and their interactions Interaction The interactions are manually curated from literature
MINT Collection of molecular interactions extracted from literature Interaction The interactions are experimentally verified
STRING A database about functional interactions for various organisms Interaction The interactions are either experimental determined or computationally predicted
KEGG A resource about high-level functional information for distinct organisms Pathway The interactions are manually drawn and predicted
Tab.1  Popular resources about different types of molecular networks.
Fig.2  The example of the smoothing process. The black and red values respectively represent the weights before and after smoothing.
Node biomarker Edge biomarker Network biomarker
Utilization of molecular network Network information is used to evaluate the impact of individual nodes. The network information is used to detect the rewired interactions. The network is used as the context to detect the dysfunctional modules in the network.
Advantages More simple and applicable The function interactions between pairs of nodes, i.e., edges, are considered The functional interactions among nodes are considered and have more predictive power.
Disadvantages The functional relationships among nodes is not considered. Edges are usually considered independently. Difficult to interpret and cannot be used directly in clinics
Tab.2  The characters, advantages and disadvantages of 3 kinds of network-based biomarkers.
Tool Biomarker type Description
NetworkAnalyst Node biomarker, network biomarker Analysis of degree and betweenness of nodes, detection of modules and shortest paths, and functional enrichment analysis
DGscore Node biomarker Detection of driver genes with a score for each sample
OncoIMPACT Node biomarker Quantify the impact of candidate driver mutations with genes connected to them
Network-based stratification (NBS) Node biomarker The mutation profiles are smoothed over the network and the subtypes are detected
stSVM Node biomarker Both mRNA and miRNA profiles were smoothed, which are further used for classification
DRAGEN Edge biomarker The identification of significant differentially regulated gene sets between different phenotypes
ProMISe Edge biomarker The detection of miRNA–mRNA interaction signatures for diagnosis
miR_Path Network biomarker The cancer-related miRNAs are detected
HyperModules Network biomarker The module biomarkers with frequently mutated genes are detected
FERAL Network biomarker Multiple operators are used to quantify the sets of connected genes as biomarker
Tab.3  Tools for the identification of the network-based biomarkers.
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