Identifying network-based biomarkers of complex diseases from high-throughput data.

  title={Identifying network-based biomarkers of complex diseases from high-throughput data.},
  author={Zhiping Liu},
  journal={Biomarkers in medicine},
  volume={10 6},
  • Zhiping Liu
  • Published 20 January 2016
  • Biology
  • Biomarkers in medicine
In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states… 

Figures and Tables from this paper

Identifying module biomarkers of hepatocellular carcinoma from gene expression data
The gene coexpressions with network model are described and genes that are closely related to liver cancer infected by hepatitis virus are detected and identified as candidate biomarkers for hepatocellular carcinoma by bioinformatics and machine learning.
Analysis of Topological Parameters of Complex Disease Genes Reveals the Importance of Location in a Biomolecular Network
The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence, which highlights the importance of gene location in the integrated functional linkages.
Identifying biomarkers for breast cancer by gene regulatory network rewiring
D-GRN is a general method to meet the demand of deciphering the high-throughput data for biomarker discovery and is easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.
Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods
A robust method for discovering biomarker genes for HCC from gene expression data based on recursive feature elimination cross-validation methods based on six different classication algorithms is proposed.
VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions
An updated computational methodology named after VD-analysis is developed, which contributes to the description of dynamic disease progression and the V-structure biomarkers facilitate the treatments of disease.
Extracting proteins involved in disease progression using temporally connected networks
It is shown that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and the authors' algorithms.
Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks
A bioinformatics framework of prioritizing type 2 diabetes genes by leveraging the modified PageRank algorithm on bilayer biomolecular networks consisting an ensemble gene-gene regulatory network and an integrative protein-protein interaction network is proposed.


Network-based analysis of complex diseases.
The authors classify the existing network biology efforts to study complex diseases, such as breast cancer, diabetes and Alzheimer's disease, using high-throughput data and computational tools into several classes based on the research topics, that is, disease genes, dysfunctional pathways, network signatures and drug-target networks.
Network biomarkers reveal dysfunctional gene regulations during disease progression
Extensive studies have been conducted on gene biomarkers by exploring the increasingly accumulated gene expression and sequence data generated from high‐throughput technology. Here, we briefly report
Identifying disease genes and module biomarkers by differential interactions
A novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which can be used as robust module biomarkers.
Edge biomarkers for classification and prediction of phenotypes
In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among
Network medicine: a network-based approach to human disease
Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
BiomarkerDigger: A versatile disease proteome database and analysis platform for the identification of plasma cancer biomarkers
The newly developed BiomarkerDigger system is useful for multi‐level synthesis, comparison, and analyses of data sets obtained from currently available web sources and demonstrates the application of this resource to the identification of a serological biomarker for hepatocellular carcinoma by comparison of plasma and tissue proteomic data sets from healthy volunteers and cancer patients.
Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity
A strategy based on discriminative area of module activities to identify gene biomarkers which interconnect as a subnetwork or module by integrating gene expression data and protein-protein interactions can efficiently identify robust and functionally meaningful module biomarkers in T2DM and could be employed in biomarker discovery of other complex diseases characterized by expression profiles.
Research and applications: An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer
A novel network-based approach to identify putative causal module biomarkers of complex diseases by integrating heterogeneous information, for example, epigenomic data, gene expression data, and protein-protein interaction network finds that aberrant DNA methylation of genes encoding TF considerably contributes to the activity change of some genes.
Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach
Modelling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules.