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Biologically-aware Latent Dirichlet Allocation (BaLDA) for the Classification of ExpressionMicroarray

机译:生物感知的潜在狄利克雷分配(BaLDA)用于表达的 r nMicroarray分类

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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.
机译:主题模型最近被证明是分析微阵列实验的真正有用的工具。特别是,它们已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。然而,在后一种情况下,基因之间功能独立性的基本假设是有限的,因为关于基因相互作用的许多其他先验信息可能是可用的(共同调节,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。拟议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。

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