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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Zhang X,Gao L,Liu ZP,Chen Ldoi
10.1186/s12859-015-0519-ysubject
Has Abstractpub_date
2015-03-18 00:00:00pages
92issn
1471-2105pii
10.1186/s12859-015-0519-yjournal_volume
16pub_type
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