Evolutionary layered hypernetworks for identifying microRNA-mRNA regulatory modules

• Published in: IEEE Congress on Evolutionary Computation pp. 1 - 8
• Main Authors: Soo-Jin Kim, Jung-Woo Ha, Lee, Bado, Byoung-Tak Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2010
• Subjects: Accuracy; Bayesian methods; Biological system modeling; Buildings; Cancer; Classification algorithms; Training
• ISBN: 1424469090; 9781424469093
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/CEC.2010.5586479

Exploring micro RNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method for identifying functional miRNA-mRNA modules from heterogeneous expression data. The proposed approach is layered hypernetworks consisting of two layers which are the layer of modality-dependent hypernetworks and of an integrating hypernetwork. The layered hypernetwork model is suitable for detecting relationships between heterogeneous modalities. Applied to the analysis of miRNA and mRNA expression profiles on multiple human cancers, the proposed model identifies oncogenic miRNA-mRNA regulatory modules. The experimental results show that our method provides a competitive performance to support vector machines, and outperforms other standard machine learning algorithms. The biological significance of the discovered miRNA-mRNA modules were validated by literature reviews.