图 书 馆
用于表达微阵列分类的生物感知潜在Dirichlet分配（BaLDA）Biologically-aware Latent Dirichlet Allocation (BaLDA) for the Classification of Expression Microarray |
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| 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: | 2012-10-14 |
| 课程语种: | 英语 |
| 中文简介: |  最近显示，主题模型是用于分析微阵列实验的真正有用的工具。特别是，它们已经成功地应用于基因聚类，并且最近还成功地应用于了样品分类。但是，在后一种情况下，基因之间功能独立性的基本假设是有局限性的，因为可能还有许多其他有关基因相互作用的先验信息（共同调控，空间邻近性或其他先验知识）。在本文中，提出了一种新颖的主题模型，该模型通过整合基因分类中编码的相关性来丰富和扩展潜在狄利克雷分配（LDA）模型。提出的主题模型用于为微阵列实验推导高度信息化和判别性的表示形式。与标准主题模型相比，它的有用性已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 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-04-14：zyk |
| 最后编审: | 2021-04-14：zyk |
| 阅读次数: | 35 |