Particle swarm optimization-based support vector regression and bayesian networks applied to the toxicity of organic compounds to tadpoles (Rana japonica)

摘要：

Particle swarm optimization (PSO) is a new optimization method with strong global search capability. In present work, PSO-support vector regression (SVR) model was proposed to predict the toxicity of organic compounds to tadpoles (Rana japonica), in which PSO was used to determine free parameters of SVR. These results showed that the prediction accuracy of PSO-SVR model is higher than those mode of MLR and PLS. Moreover, Bayesian networks (BNs) was adopted to describe the relationship between toxicity associated with molecular descriptors in this work. The result of BNs was considered to be reasonable.

关键词： Rana japonica tadpoles toxicity Particle swarm optimization support vector regression Bayesian networks

作者: Qiang Su Wen-cong Lu Xu Liu Tian-hong Gu Bing Niu

作者单位: College of Material Science and Engineering Shanghai University Shanghai, China College of Life Sciences Shanghai University Shanghai, China

会议类型: 国际会议

会议名称: 2011 4th International Conference on Biomedical Engineering and Informatics(第四届生物医学工程与信息学国际会议 BMEI 2011)

会议地点: 上海

会议语种:英文

页码: 2138-2142

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）