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

Abstract:

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.

Key words: Time-dose-mortality model (TDM), complementary log-log model (CLL), support vector regression, Leave-One-Out method, bioassay