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微阵列数据的动态建模

Dynamic Modelling of Microarray Data
课程网址: http://videolectures.net/pesb07_barenco_dmo/  
主讲教师: Martino Barenco
开课单位: 伦敦大学学院
开课时间: 2007-04-04
课程语种: 英语
中文简介:
我们最近发布了rHVDM(隐藏变量动态建模),一种R / Bioconductor软件包,使用时间过程微阵列数据预测已知转录因子的目标。该算法背后的关键特征是mRNA浓度的简单ODE模型。在rHVDM的第一阶段,转录因子活性(隐藏变量)是从少数已知靶标的表达时间谱推导出的。然后该信息用于筛选依赖于该转录因子的其他基因。已经使用Affymetrix微阵列时间过程数据证明了该技术的准确性,并且使用靶向转录因子(p53)的siRNA敲低实验验证了该技术的准确性。在实现rHVDM算法并对其进行优化以供发布时,我们遇到了许多问题。这些包括参数可识别性,参数计数减少,算法速度,参数域限制,置信区间估计和测量噪声。我将逐一讨论这些问题,以及我们用于解决这些问题的技术。
课程简介: We recently released rHVDM (Hidden Variable Dynamic Modelling), an R/Bioconductor package that predicts targets of a known transcription factor using time course microarray data. The key feature behind the algorithm is a simple ODE model of mRNA concentration. In the first stage of rHVDM, transcription factor activity (the hidden variable) is deduced from the expression time profile of a small number of known targets. This information is then used to screen other genes for dependency on that transcription factor. The accuracy of the technique has been demonstrated with Affymetrix microarray time course data and verified experimentally using siRNA knockdown of a targeted transcription factor (p53). While implementing the rHVDM algorithm and refining it for release we encountered a number of problems. These included parameter identifiability, parameter count reduction, algorithmic speed, parameter domain restriction, confidence interval estimation, and measurement noise. I will discuss each of these issues individually, along with the techniques we used to address them.
关 键 词: 隐藏变量动态; 微阵列数据; 转录因子
课程来源: 视频讲座网
最后编审: 2019-09-13:lxf
阅读次数: 44