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生物学的多核学习

Multi-Kernel Learning for Biology
课程网址: http://videolectures.net/lkasok08_noble_mklfb/  
主讲教师: William Stafford Noble
开课单位: 华盛顿大学
开课时间: 2008-12-20
课程语种: 英语
中文简介:
今天生物学家面临的主要任务之一是整合不同类型实验数据提供的分子生物学的不同观点。例如,在酵母中,对于给定的基因,我们通常知道它编码的蛋白质、蛋白质与其他蛋白质的相似性、在数百种实验条件下与给定基因相关的mRNA表达水平、该基因上游区域已知或推断的转录因子结合位点的出现,以及与特定基因蛋白质产物相互作用的许多蛋白质的特性。每种不同的数据类型都提供了细胞分子机制的一个视图。内核方法允许我们以正常形式表示这些异构数据类型,并使用内核代数来同时解释多个类型的数据。因此,多核学习方法已被应用于各种生物学应用。在本文中,我将描述其中的几个应用程序,概述我们从将多核学习方法应用于实际数据中所获得的经验教训,并为这一领域的未来研究提供几种途径。
课程简介: One of the primary tasks facing biologists today is to integrate the different views of molecular biology that are provided by various types of experimental data. In yeast, for example, for a given gene we typically know the protein it encodes, that protein’s similarity to other proteins, the mRNA expression levels associated with the given gene under hundreds of experimental conditions, the occurrences of known or inferred transcription factor binding sites in the upstream region of that gene, and the identities of many of the proteins that interact with the given gene’s protein product. Each of these distinct data types provides one view of the molecular machinery of the cell. Kernel methods allow us to represent these heterogeneous data types in a normal form, and to use kernel algebra to reason about more than one type of data simultaneously. Consequently, multi-kernel learning methods have been applied to a variety of biology applications. In this talk, I will describe several of these applications, outline the lessons we have learned from applying multi-kernel learning methods to real data, and suggest several avenues for future research in this area.
关 键 词: 生物信息学; 计算机科学; 机器学习; 核方法; 多核学习
课程来源: 视频讲座网
最后编审: 2019-12-27:lxf
阅读次数: 40