Dipartimento di Informatica, Università degli Studi di Verona, Ca' Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Nov-Dec;9(6):1831-6. doi: 10.1109/TCBB.2012.121.
In recent years a particular class of probabilistic graphical models-called topic models-has proven to represent an useful and interpretable tool for understanding and mining microarray data. In this context, such models have been almost only applied in the clustering scenario, whereas the classification task has been disregarded by researchers. In this paper, we thoroughly investigate the use of topic models for classification of microarray data, starting from ideas proposed in other fields (e.g., computer vision). A classification scheme is proposed, based on highly interpretable features extracted from topic models, resulting in a hybrid generative-discriminative approach; an extensive experimental evaluation, involving 10 different literature benchmarks, confirms the suitability of the topic models for classifying expression microarray data.
近年来，一类特殊的概率图模型——主题模型——已被证明是理解和挖掘基因芯片数据的一种有用且可解释的工具。在这种情况下，这些模型几乎只应用于聚类场景，而分类任务则被研究人员忽视了。在本文中，我们从其他领域（例如计算机视觉）提出的想法出发，深入研究了主题模型在基因芯片数据分类中的应用。我们提出了一种分类方案，该方案基于从主题模型中提取的高度可解释的特征，从而形成一种混合生成式-判别式方法；通过涉及 10 个不同文献基准的广泛实验评估，证实了主题模型在分类表达基因芯片数据方面的适用性。
