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CEC
2007
IEEE

Evolving hypernetwork classifiers for microRNA expression profile analysis

10 years 3 months ago
Evolving hypernetwork classifiers for microRNA expression profile analysis
Abstract-- High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on `hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by liter...
Sun Kim, Soo-Jin Kim, Byoung-Tak Zhang
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2007
Where CEC
Authors Sun Kim, Soo-Jin Kim, Byoung-Tak Zhang
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