Wei-Che Hwang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Wei-Che Hwang has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Wei-Po Lee
Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
1 shared publication - Yu-Ting Hsiao
Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
1 shared publication
Latest Publications
- Open Access
- 1 January 2014
- Published by Springer Nature in BMC Systems Biology
- Vol. 8 (1) , 5
Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment
To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks.
- 1 January 2014
- Published by Springer Nature in BMC Systems Biology
- Vol. 8 (1) , 5
Open AccessDesigning a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment
To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks.