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Hypernetwork Classifiers for Microarray-Based miRNA Module Analysis  

Kim, Sun (서울대학교 컴퓨터공학부)
Kim, Soo-Jin (서울대학교 생물정보학 협동과정)
Zhang, Byoung-Tak (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.6, 2008 , pp. 347-356 More about this Journal
Abstract
High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.
Keywords
hypernetworks; miRNA module analysis; microarrays; classification;
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