Name
Abstract | ||
|---|---|---|
The inference of genetic networks using a model based on a set of differential equations is generally time-consuming. In order to decrease its computational time, we have proposed the inference method using a Normalized Gaussian network (NGnet) model. The inferred models however contain many false-positive regulations when we apply the NGnet approach to the genetic network inference problems. This paper proposes the reduced NGnet model and the gradual reduction strategy to overcome the drawbacks of the NGnet approach. Then, in order to verify their effectiveness, we apply the inference method using the proposed techniques to several artificial genetic network inference problems. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IJCNN.2007.4371083 | 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 |
Keywords | Field | DocType |
differential equation,differential equations,false positive,gaussian processes,generation time,genetics | Reduction strategy,Differential equation,Normalization (statistics),Inference,Computer science,Gaussian,Artificial intelligence,Gaussian process,Biological network inference,Genetic network,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 1 | 0.38 |
References | Authors | |
10 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuhei Kimura | 1 | 204 | 15.99 |
| Katsuki Sonoda | 2 | 24 | 2.37 |
| Soichiro Yamane | 3 | 24 | 2.37 |
| Kotaro Yoshida | 4 | 14 | 2.61 |
| Koki Matsumura | 5 | 50 | 4.73 |
| Mariko Hatakeyama | 6 | 175 | 12.17 |