使用调控模型的基因表达测量的目标检索Targeted retrieval of gene expression measurements using regulatory models |
|
课程网址: | http://videolectures.net/mlsb2012_georgii_retrieval/ |
主讲教师: | Elisabeth Georgii |
开课单位: | 国赫尔辛基科技机构 |
开课时间: | 信息不详。欢迎您在右侧留言补充。 |
课程语种: | 英语 |
中文简介: | **动机:**基因表达测量的大型公共存储库提供了将新实验定位到早期研究的背景中的机会。虽然以前的方法依赖于实验注释或跨基因或基因组的表达谱的全局相似性,但我们通过基于围绕感兴趣的预定基因的非监督的、数据驱动的调节模型测量相似性来比较实验。我们的实验检索方法在两个概念方面是新颖的:(i)有针对性的焦点和可解释性:分析针对的是与分析者相关或来自先验知识的基因的调节关系;(i i)基于调节模型的相似性度量:基于inf的强度检索相关实验。\\\\\\\结果:**我们从数据仓库中学习了用于调节特定基因的模型,并利用该模型构建信息检索任务的相似性度量。我们使用Fisher内核,这是一种严格的相似性度量,通常应用于识别分类器中的生成模型。人类和植物微阵列收集的结果表明,我们的方法能够大大提高相关实验的检索标准方法。此外,它允许用户根据链接活动模式的变化来解释生物学条件。我们对梭菌渗透胁迫网络的研究表明,该方法成功地识别了给定关键基因的相关关系。 |
课程简介: | **Motivation:** Large public repositories of gene expression measurements offer the opportunity to position a new experiment into the context of earlier studies. While previous methods rely on experimental annotation or global similarity of expression profiles across genes or gene sets, we compare experiments by measuring similarity based on an unsupervised, data-driven regulatory model around pre-specified genes of interest. Our experiment retrieval approach is novel in two conceptual respects: (i) targetable focus and interpretability: the analysis is targeted at regulatory relationships of genes that are relevant to the analyst or come from prior knowledge; (ii) regulatory model-based similarity measure: related experiments are retrieved based on the strength of inferred regulatory links between genes.\\ **Results:** We learn a model for the regulation of specific genes from a data repository, and exploit it to construct a similarity metric for an information retrieval task. We use the Fisher kernel, a rigorous similarity measure that typically has been applied to utilize generative models in discriminative classifiers. Results on human and plant microarray collections indicate that our method is able to substantially improve the retrieval of related experiments against standard methods. Furthermore, it allows the user to interpret biological conditions in terms of changes in link activity patterns. Our study of the osmotic stress network for A. thaliana shows that the method successfully identifies relevant relationships around given key genes.\\ |
关 键 词: | 机器学习; 计算机科学; 生物信息学 |
课程来源: | 视频讲座网 |
最后编审: | 2021-12-23:liyy |
阅读次数: | 38 |