Name
Abstract | ||
|---|---|---|
We propose a new method for the inference of the genetic networks. The proposed method uses a neural network model to describe the genetic network. The inference of the neural network model of the genetic network is defined as the function optimization problem. As the function optimizer for this problem, a genetic local search is used. At this time, to enhance the probability of finding a reasonable solution, we introduce a priori knowledge about the genetic network into the objective function. In this study, we also propose the method based on the sensitivity analysis to interpret the optimized neural network model. Through artificial genetic network inference problems, we verify the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CEC.2005.1554898 | 2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS |
Keywords | Field | DocType |
objective function,sensitivity analysis,a priori knowledge,local search,neural network model,genetic algorithms,neural nets,genetics | Computer science,Meta-optimization,Stochastic neural network,Recurrent neural network,Probabilistic neural network,Genetic representation,Artificial intelligence,Adaptive neuro fuzzy inference system,Deep learning,Biological network inference,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuhei Kimura | 1 | 204 | 15.99 |
| Katsuki Sonoda | 2 | 24 | 2.37 |
| Soichiro Yamane | 3 | 24 | 2.37 |
| Koki Matsumura | 4 | 50 | 4.73 |
| Mariko Hatakeyama | 5 | 175 | 12.17 |