ArticleBMC bioinformatics2015
Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 22 citations in OpenAlex.
- Capsaicin and Zinc Promote Glucose Uptake in C2C12 Skeletal Muscle Cells through a Common Calcium Signalling Pathway.International journal of molecular sciences · 2022Article
- Data analysis methods for defining biomarkers from omics data.Analytical and bioanalytical chemistry · 2022Review
- ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.Scientific reports · 2018Article
- Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer.BMC bioinformatics · 2016Article
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying 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.
resultsWe 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.
conclusionThe 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.
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