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使用马尔可夫链蒙特卡罗的转录因子网络的高斯过程建模
Gaussian process modelling of transcription factor networks using Markov Chain Monte-Carlo |
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| 课程网址: |
http://videolectures.net/licsb08_titsias_gpm/
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| 主讲教师: |
Michalis K. Titsias
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| 开课单位: |
曼彻斯特大学
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| 开课时间: |
2008-04-17
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| 课程语种: |
英语
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| 中文简介: |
常微分方程（ODE）可以为动力学建模提供有用的框架生物网络。在这项研究中，我们专注于一个小的生物子系统，其中有一套目标基因受一种转录因子蛋白的调节。蛋白质和基因的浓度特定的动力学参数，如基础率，衰变率和敏感性通常是未知的。建模的目的是通过利用一组观察到的基因表达水平来估计这些量。我们考虑贝叶斯框架来建模基于高斯的ODE系统流程。高斯过程用作转录因子蛋白的先验，并允许我们以时间连续的方式推断蛋白质的浓度。我们提出马尔可夫链蒙特卡罗算法的完整贝叶斯统计推断。我们的MCMC算法的基本特性是我们通过对高斯过程模型应用新的采样算法来有效地推断蛋白质浓度。我们将我们的技术应用于线性和非线性模型。
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| 课程简介: |
Ordinary differential equations (ODEs) can provide an useful framework for modelling the dynamics
of biological networks. In this study, we focus on a small biological sub-system where a set of target
genes are regulated by one transcription factor protein. The concentration of the protein and the gene
specific kinetic parameters such as basal rates, decay rates and sensitivities are typically unknown. The objective of modelling is to estimate these quantities by making use of a set of observed gene expression levels.
We consider a Bayesian framework for modelling the system of ODEs that is based on Gaussian
processes. The Gaussian process is used as the prior for the transcription factor protein and allows us to infer the concentration of the protein in a time continuous manner. We present a Markov chain Monte Carlo algorithm for a full Bayesian statistical inference. The essential property of our MCMC algorithm is that we efficiently infer the protein concentration by applying a novel sampling algorithm for Gaussian process models. We apply our technique to linear and non-linear models.
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| 关 键 词: |
常微分方程; 框架生物网络; 高斯过程
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-05-13：cjy
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| 阅读次数: |
81
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