A new structure learning method for construting gene networks
Bayesian networks can be used to model gene regulatory networks because of its capability of capturing causal relationships between genes. However, learning Bayesian network is an NP-hard problem. Hill climbing methods are used in BN learning, in which K2 is a frequently used greedy search algorithm. But the performance of K2 algorithm is greatly affected by a prior ordering of input nodes and relatively low accuracy of the learned structures may be observed. To solve these problems, we propose a new algorithm (BPSO_BN) to explore the use of Binary Particle Swarm Optimization (BPSO) algorithms for learning Bayesian networks. The result of experiments show that our BPSO based algorithm can obtain better networks than hill climbing methods. BPSO_BN also shows the effectiveness for network reconstruction to gene expression data measured during the yeast cell cycle.
Bayesian networks Binary particle swarm optimization (BPSO) Structure learning gene networks
Zhihua Du Yiwei Wang Zhen Ji
School of Information Engineering ShenZhen University Shenzhen,China
国际会议
北京
英文
1-4
2009-06-11(万方平台首次上网日期,不代表论文的发表时间)