Constructing biological networks is one of the most important issues in systems biology. However, constructing a network from data manually takes a considerable large amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques, genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been conducted and the preliminary results show that both approaches can be used to infer networks successfully.
Genetic programming and neural networks are combined to evolve gene regulatory network models in Lee and Yang REF .
Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data
Genomics, Proteomics & Bioinformatics
Genomics, Proteomics & Bioinformatics
Computer Science Medicine