Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms

Background: Modeling cancer-related regulatory modules from gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as developments of new diagnose and therapies. Several mathematical models have been used to explore the phenomena of transcriptional regulatory mechanisms in Saccharomyces cerevisiae. However, the contemplating on controlling of feed-forward and feedback loops in transcriptional regulatory mechanisms is not resolved adequately in Saccharomyces cerevisiae, nor is in human cancer cells. Results: In this study, we introduce a Genetic Algorithm-Recurrent Neural Network (GA-RNN) hybrid method for finding feed-forward regulated genes when given some transcription factors to construct cancer-related regulatory modules in human cancer microarray data. This hybrid approach focuses on the construction of various kinds of regulatory modules, that is, Recurrent Neural Network has the capability of controlling feed-forward...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Jung-Hsien Chiang, Shih-Yi Chao
• Keywords: BMCBI 2007, Cancer-related Regulatory Modules, Regulatory Module, Transcriptional Regulatory Mechanisms