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生物网络的统计学习:简要概述

Statistical learning of biological networks: a brief overview
课程网址: https://videolectures.net/videos/licsb08_dalche_slb  
主讲教师: Florence d'Alche-Buc
开课单位: 信息不详。欢迎您在右侧留言补充。
开课时间: 2008-04-17
课程语种: 英语
中文简介:
识别生物网络,如信号通路、基因调控网络、蛋白质-蛋白质相互作用网络和代谢网络被认为是计算生物学中的一个关键挑战。使用机器学习框架,这个问题可以用不同的观点来解决,当然这取决于要推断的生物相互作用的性质,也取决于所选模型的抽象水平和可用的先验知识的数量。自2000年以来,生物网络统计学习的研究产生了一个丰富的方法小组,其兴趣超过了计算生物学领域。网络识别已经使用大规模数据挖掘方法、监督预测方法和反向建模方法来解决。在这个唯一的最后一个家族中,关注迄今为止已经提出的众多图形模型是非常有启发性的,例如图形高斯模型、贝叶斯网络、动态贝叶斯网络和状态空间模型。我将简要回顾这些方法,讨论模型复杂性、与生物学的相关性、处理隐藏变量的能力和可扩展性等问题。我还将呼吁建立一个基准存储库,专门用于相关测试问题的示例,即使真正相关的测试总是在体内或体外进行。
课程简介: Identification of biological networks such as signalling pathways, gene regulatory networks, protein-protein interaction networks and metabolic networks is considered as a key challenge in computational biology. Using machine learning framework, this problem can be addressed using different points of view, depending of course on the nature of the biological interactions to be inferred but also on the level of abstraction of the chosen modeling and the amount of prior knowledge available. Since 2000, research in statistical learning of biological networks have given rise to a rich panel of approaches whose interest overcomes the field of computational biology. Network identification has been tackled using large scale data-mining approaches, supervised predictive approaches and reverse-modeling approaches. In this sole last family, it is very instructive to focus on the numerous graphical models that have been proposed so far such as Graphical Gaussian Models, Bayesian networks, Dynamical Bayesian networks and state-space models. I will present a short review of these methods discussing among other issues model complexity, relevance to biology, ability to deal with hidden variables and scalability. I will also plead for the construction of a benchmark repository devoted to examples of relevant test problems even if the true relevant test has always to be made in vivo or in vitro.
关 键 词: 识别生物网络; 基因调控网络; 蛋白质-蛋白质相互作用网络和代谢网络
课程来源: vidiolectures
数据采集: 2025-02-23:yuhongrui
最后编审: 2025-02-23:yuhongrui
阅读次数: 2