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

ABSTRACT:

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 regulator

SUBMITTER: Chiang JH 

PROVIDER: S-EPMC1838431 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature