摘要
生物测定是生物学、医学、毒理学的重要内容与基础。常用的定量生物测定数据分析方法时间-剂量-死亡率模型(TDM)不能对复杂生测数据建立统一模型,信息利用不充分。本文基于支持向量回归(SVR),提出了一种能对不同供试因子、不同供试对象和不同环境条件下复杂生测数据统一建模的新方法。14个简单生测数据和2套复杂生测数据的对比分析结果表明,SVR模型拟合与留一法预测精度均优于TDM模型,估计的LD50和LT50等指标更为可信。SVR模型有望作为TDM模型的有益补充,在定量生物测定数据分析中得到广泛应用。
Bioassay plays an important role in the studies of biology,medicine and toxicology. The time-dose-mortality model （TDM） widely applied to quantitative bioassay data analysis can not construct a unified model for complex bioassay data,and has the disadvantage of utilizing the information incompletely. Based on support vector regression （SVR）,a novel quantitative bioassay model has been developed,which can construct a unified model for complex data with different test factors,different test objects and different environment factors. We compared the prediction performance between SVR and TDM using 14 simple data and 2 complex data. The results showed that SVR achieved better precision than TDM not only in self-consistency test but also in jackknife test,implying that the estimated values of LD50 and LT50 by the former are more reliable. As a useful supplement to TDM,SVR has the potential to be widely used for quantitative bioassay data analysis.
出处
《昆虫学报》
CAS
CSCD
北大核心
2010年第12期1436-1441,共6页
Acta Entomologica Sinica
基金
湖南省杰出青年基金(10JJ1005)
湖南省教育厅青年基金(05B025)
湖南省教育厅科研项目(09C514)
湖南省高校科技创新团队项目
关键词
时间-剂量-死亡率模型
互补重对数模型
支持向量回归
留一法
生物测定
Time-dose-mortality model （TDM）
complementary log-log model （CLL）
support vector regression
Leave-One-Out method
bioassay
