Abstract
Dysregulation of miRNA–mRNA regulatory networks is very common phenomenon in any diseases including cancer. Altered expression of biomarkers leads to these gynecologic cancers. Therefore, understanding the underlying biological mechanisms may help in developing a robust diagnostic as well as a prognostic tool. It has been demonstrated in various studies that the pathways associated with gynecologic cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA–mRNA regulatory modules may help in understanding the mechanism of altered gynecologic cancer pathways. In this regard, an existing robust mutual information-based Maximum-Relevance Maximum-Significance algorithm has been used for identification of miRNA–mRNA regulatory modules in gynecologic cancer. A set of miRNA–mRNA modules are identified first than their association with gynecologic cancer are studied exhaustively. The effectiveness of the proposed approach is compared with the existing methods. The proposed approach is found to generate more robust integrated networks of miRNA–mRNA in gynecologic cancer.
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Acknowledgements
This work is partially supported by the seed grant program of the Indian Institute of Technology Jodhpur, India (grant no. I/SEED/SPU/20160010). The author wants to acknowledge Mr. Shubham Talbar, Indian Institute of Technology Jodhpur, India for his contribution in implementing certain bioinformatics tools.
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Paul, S. (2019). Integration of miRNA and mRNA Expression Data for Understanding Etiology of Gynecologic Cancers. In: Lai, X., Gupta, S., Vera, J. (eds) Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8982-9_13
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DOI: https://doi.org/10.1007/978-1-4939-8982-9_13
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