1. 國立陽明大學生資學程 陳虹瑋

2. Genetic Algorithm Background Fitness function 010101 ……. population 010101 selection Cross over mutation Fitness values Random cross over value Random mutation value Next generation

3. The objective of this paper Reverse engineering : –To predict a regulating network structure pf the interacting genes from observed gene expression pattern. It consists of modeling the rules of regulation. The GA is applied to train the model with observed data to predict the regulatory pathways, represented as influence matrix. challenging featuresSolving Some challenging features

4. Gene expression and regulations

5. Modeling the rules of regulation

6. GA Implementations

7. Fitness function

8. Parametric Optimization

9. Network inference under some condition Conditions : –N indicates the number of nodes in the network; –connectivity k is the maximum number of inputs per gene; – GA parameters

10. Result N = 7 condition :

11. Result. N=10, and the sensitivity was 0.929 and specificity was 1.00 for this model. The maximum error in the influence values is less than 30 %.

12. Result.