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使用基于树的方法从表达数据中推断出监管网络

Inferring regulatory networks from expression data using tree-based methods
课程网址: http://videolectures.net/mlsb2010_huynh_thu_irn/  
主讲教师: Van Anh Huynh-Thu
开课单位: 列日大学
开课时间: 2010-11-08
课程语种: 英语
中文简介:
计算系统生物学的一个紧迫的开放问题是使用高通量基因组数据,特别是微阵列基因表达数据,对遗传调控网络(GRN)的拓扑结构进行了研究。反向工程评估和方法对话(DREAM)的挑战是评估GRN推理算法在模拟数据基准测试中的成功[11-13]。在本文中,我们提出了一种新的GRN推理算法,该算法在DREAM4 In Silico Multifactorialchallenge3中表现最佳。此外,我们证明该算法与现有算法相比有利于解读大肠杆菌的遗传调控网络。它没有对基因调控的性质做出任何假设,可以处理组合和非线性相互作用,产生定向GRN,并且是快速的这种作品的扩展版本出现在[7]中。
课程简介: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data [11–13]. In this article, we present a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge3. In addition, we show that the algorithm compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn’t make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. An extended version of this works appears in [7].
关 键 词: 计算系统生物学; 高通量基因组; 拓扑结构
课程来源: 视频讲座网
最后编审: 2021-12-22:liyy
阅读次数: 45