Abstract
High-throughput biological monitoring method can simultaneously detect thousands of parameters of the same sample, and its application in biomedicine is more and more widespread. However, how to systematically analyze and extract useful information from high-throughput data remains an important issue. The emergence of network biology makes people have a deeper understanding of complex biological systems, and the implementation of tissue/cell functions has modular characteristics. At present, related networks are increasingly used in bioinformatics. Weighted gene co-expression network analysis, WGCNA is a systematic biological tool for describing gene expression patterns in samples. In this review, the progress in disease classification and prognosis, pathogenesis and other related fields is systematically reviewed. Firstly, the principle, analysis process, advantages and disadvantages are summarized. Secondly, it introduces how to annotate diseases, normal tissues, drugs, evolution and genome. Finally, the new application space is forecasted with the new high throughput technology. It is hoped that researchers will have a better understanding of the application of WGCNA.
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Change history
20 May 2020
Retraction Note to:Chapter “Application of Weighted Gene Co-expression Network Analysis in Biomedicine” in: C. Huang et al. (eds.), Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), Advances in Intelligent Systems and Computing 1088
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Acknowledgements
This work was supported by grants from The National Natural Science Foundation of China (No. 61862056), the Guangxi Natural Science Foundation (No. 2017GXNSFAA198148) foundation of Wuzhou University (No. 2017B001) and Guangxi Colleges and Universities Key Laboratory of Professional Software Technology, Wuzhou University.
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Zheng, M., Zhuo, M. (2020). RETRACTED CHAPTER: Application of Weighted Gene Co-expression Network Analysis in Biomedicine. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_93
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DOI: https://doi.org/10.1007/978-981-15-1468-5_93
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