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用生物意识的隐含狄利克雷分布来表示微阵列的分类

Biologically-aware Latent Dirichlet Allocation (BaLDA) for the Classification of Expression Microarray
课程网址: http://videolectures.net/prib2010_bicego_blda/  
主讲教师: Manuele Bicego
开课单位: 维罗纳大学
开课时间: 2010-10-14
课程语种: 英语
中文简介:
主题模型最近被证明是用于微阵列实验分析的真正有用的工具。特别是,它们已经成功地应用于基因聚类,最近,也应用于样本分类。然而,在后一种情况下,基因间功能独立性的基本假设是有限的,因为许多其他关于基因相互作用的先验信息可能是可用的(共调节、空间邻近或其他先验知识)。本文提出了一种新的主题模型,该模型通过将潜在的dirichlet分配(lda)模型整合到基因分类中,丰富和扩展了这种依赖关系。提出的主题模型用于微阵列实验的高信息性和判别性表示。与标准主题模型相比,它的实用性在两种不同的分类测试中得到了证明。
课程简介: Topic models have recently shown to be really useful tools for the analysis of microarray experiments. In particular they have been successfully applied to gene clustering and, very recently, also to samples classification. In this latter case, nevertheless, the basic assumption of functional independence between genes is limiting, since many other a priori information about genes’ interactions may be available (co-regulation, spatial proximity or other a priori knowledge). In this paper a novel topic model is proposed, which enriches and extends the Latent Dirichlet Allocation (LDA) model by integrating such dependencies, encoded in a categorization of genes. The proposed topic model is used to derive a highly informative and discriminant representation for microarray experiments. Its usefulness, in comparison with standard topic models, has been demonstrated in two different classification tests.
关 键 词: 微阵列实验; 基因聚类; 隐含狄利克雷分配
课程来源: 视频讲座网
最后编审: 2020-06-03:毛岱琦(课程编辑志愿者)
阅读次数: 49