Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity
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- 作者：Xindong Zhang (1) (2)
Lin Gao (1) (4)
Zhi-Ping Liu (3)
Luonan Chen (2) (4) (5)
1. School of Computer Science and Technology ; Xidian University ; Xi鈥檃n ; 710000 ; China
2. Key Laboratory of Systems Biology ; Institute of Biochemistry and Cell Biology ; Shanghai Institutes for Biological Sciences ; Chinese Academy of Sciences ; Shanghai ; 200031 ; China
4. Institute of Industrial Science ; University of Tokyo ; Tokyo ; 153-8505 ; Japan
3. Department of Biomedical Engineering ; School of Control Science and Engineering ; Shandong University ; Shandong ; 250061 ; China
5. School of Life Science and Technology ; ShanghaiTech University ; Shanghai ; 201210 ; China - 关键词：Computational biology ; Gene expression profiling ; Network biomarker ; Type 2 diabetes mellitus
- 刊名：BMC Bioinformatics
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：16
- 期：1
- 全文大小：1,470 KB
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- 出版者：BioMed Central
- ISSN：1471-2105
文摘
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.