图 书 馆
高斯过程回归引导Gaussian process regression bootstrapping |
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| 课程网址: | http://videolectures.net/licsb09_kirk_gprb/ |
| 主讲教师: | Paul Kirk |
| 开课单位: | 帝国理工学院 |
| 开课时间: | 2009-04-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Both mechanistic and empirical modelling techniques are employed in systems biology. The former construct models whose structure explicitly describes components of the biological system under investigation, while the latter make predictions on the strength of patterns in the data. Although empirical models such as Gaussian process regression (GPR) do not directly help us to elucidate the processes that generated a given data set, they can nevertheless form part of a strategy for testing and investigating hypotheses and mechanistic models. , In our work, we exploit the predictive power of GPR in order to generate plausible simulated data sets from experimentally obtained time-course data. This amounts to a parametric bootstrap (in which the parametric model is a multivariate normal) that implicitly takes into account the time-dependence in the data. Having obtained bootstrap samples, we fit mechanistic models to both the original and simulated data. The variability amongst these fitted models reveals the sensitivity of the fit to uncertainty in the data. We use this approach to investigate the effects of data uncertainty upon parameter estimates in a model of a signalling pathway and upon gene network inference. |
| 关 键 词: | 高斯过程回归; 参数模型; 机械模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-01-16：chenxin |
| 阅读次数: | 152 |