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Machine Learning Approach for Identification of miRNA-mRNA Regulatory Modules in Ovarian Cancer
, S. Talbar
Published in Springer Verlag
Volume: 10597 LNCS
Pages: 438 - 447
Ovarian cancer is a fatal gynecologic cancer. Altered expression of biomarkers leads to this deadly cancer. 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 the pathways associated with ovarian cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered ovarian 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 ovarian cancer. A set of miRNA-mRNA modules are identified first than their association with ovarian cancer are studied exhaustively. The effectiveness of the proposed approach is compared with existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in ovarian cancer. © 2017, Springer International Publishing AG.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetSpringer Verlag
Open AccessNo