Reverse Engineering Module Networks by PSO-RNN Hybrid Modeling

Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules. Results: This study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is...

• Type: Conference
• Year: 2008
• Where: BIOCOMP
• Authors: Yuji Zhang, Jianhua Xuan, Benildo de los Reyes, Robert Clarke, Habtom W. Ressom
• Keywords: BIOCOMP 2008, Bioinformatics, Gene, Gene Expression Data, Inference