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探索使用扰动和贝叶斯连续学习策略的网络推理的实验设计
Exploring experimental designs for network inference using perturbations and a Bayesian sequential learning strategy |
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| 课程网址: |
http://videolectures.net/licsb09_penfold_eed/
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| 主讲教师: |
Christopher Penfold
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| 开课单位: |
华威大学
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| 开课时间: |
2009-04-16
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| 课程语种: |
英语
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| 中文简介: |
现代的系统生物学方法要求计算模型的紧密耦合迭代循环和模型预测的独立实验验证。模型推理的贝叶斯公式应该可以例外地适应这种类型的实验范式。已知已编码到模型中的先验知识，我们可以根据实验A中的数据训练模型。结果是后验分布，例如基因调控网络，它可以作为下一个模型的先验，根据实验B中的数据训练。每个阶段的贝叶斯模型可以看作是实验数据的蒸馏。到那时为止获得的，并且由于它是一个概率模型，它可以作为专家优先用于在下一个数据集上训练的模型。因此，可以采用贝叶斯顺序学习策略，而不是等待在训练第一个模型之前收集所有数据。我们利用现实的硅片模型网络的模拟数据和研究拟南芥和大肠杆菌应激反应的实验微阵列时间序列数据集来探索这一模式。
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| 课程简介: |
Modern approaches to systems biology call for a tightly coupled iterative cycle of computational modelling and independent experimental validation of model predictions. A Bayesian formulation to model inference should be exceptionably amenable to this type of experimental paradigm. Given prior knowledge that has been encoded into a model, we can train the model on data from experiment A. The result is a posterior distribution over, say, gene regulatory networks which can act as a prior for the next model, trained on data from experiment B. The Bayesian model at each stage can be seen as a distillation of the experimental data obtained up to that point, and since it is a probabilistic model it can be used as an expert prior for the model trained on the next data set. A Bayesian sequential learning strategy can therefore be employed, instead of waiting for all the data to be collected before training the first model. We explore this paradigm using simulated data from a realistic in silico model network and experimental microarray time series data sets studying stress responses in Arabidopsis and E. coli.
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| 关 键 词: |
现代系统生物学; 迭代周期; 贝叶斯公式; 基因调控网络
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-12-27：lxf
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| 阅读次数: |
46
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