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Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity.

Zhang X, Gao L, Liu ZP, Chen L - BMC Bioinformatics (2015)

Bottom Line: This module biomarker is enriched with known causal genes and related functions of T2DM.Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.The proposed approach 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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Xidian University, Xi'an, 710000, China. zxd841@163.com.

ABSTRACT

Background: Identifying diagnosis and prognosis biomarkers from expression profiling data is of great significance for achieving personalized medicine and designing therapeutic strategy in complex diseases. However, the reproducibility of identified biomarkers across tissues and experiments is still a challenge for this issue.

Results: We propose 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. Then, we implement the procedure in T2DM as a case study and identify a module biomarker with 32 genes from mRNA expression data in skeletal muscle for T2DM. This module biomarker is enriched with known causal genes and related functions of T2DM. Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.

Conclusion: The proposed approach 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.

Show MeSH
Overview of the proposed framework for identifying module biomarker.
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Related In: Results  -  Collection

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Fig1: Overview of the proposed framework for identifying module biomarker.

Mentions: FigureĀ 1 shows the flowchart of our method for identifying module biomarker. The main idea is that genes function as modules, and the activity of group of genes or modules may be enhanced or weakened by their interactors.Figure 1


Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity.

Zhang X, Gao L, Liu ZP, Chen L - BMC Bioinformatics (2015)

Overview of the proposed framework for identifying module biomarker.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4374500&req=5

Fig1: Overview of the proposed framework for identifying module biomarker.
Mentions: FigureĀ 1 shows the flowchart of our method for identifying module biomarker. The main idea is that genes function as modules, and the activity of group of genes or modules may be enhanced or weakened by their interactors.Figure 1

Bottom Line: This module biomarker is enriched with known causal genes and related functions of T2DM.Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.The proposed approach 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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Xidian University, Xi'an, 710000, China. zxd841@163.com.

ABSTRACT

Background: Identifying diagnosis and prognosis biomarkers from expression profiling data is of great significance for achieving personalized medicine and designing therapeutic strategy in complex diseases. However, the reproducibility of identified biomarkers across tissues and experiments is still a challenge for this issue.

Results: We propose 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. Then, we implement the procedure in T2DM as a case study and identify a module biomarker with 32 genes from mRNA expression data in skeletal muscle for T2DM. This module biomarker is enriched with known causal genes and related functions of T2DM. Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.

Conclusion: The proposed approach 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.

Show MeSH