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• W2183622060 abstract "Motivation: DNA microarray technologies make it possible to simultaneously monitor thousands of genes expression levels. A topic of great interest here is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression level. Currently, various clustering methods have been proposed in the literature to classify cancer and normal samples based on microarray data, and they are dominantly data-driven approaches. In this paper, we propose an alternative approach, a model-driven approach. Results: In this work, we propose an ensemble dependence model, aiming at exploring the group dependence relationship of gene clusters. Under the framework of hypothesis-testing, we employ genes’ dependence relationship as a feature to model and classify cancer and normal samples. The proposed classification scheme is applied to three real cancer data sets, and it is noted that the proposed method yields very promising performance. We further discuss the underlying working principle of the proposed method based on eigen-analysis, and demonstrate different patterns of cancer and normal samples. In addition, we examine the effects of possible model-mismatch on the proposed scheme. Availability: contact the authors" @default.
• W2183622060 created "2016-06-24" @default.
• W2183622060 creator A5016488771 @default.
• W2183622060 creator A5064470352 @default.
• W2183622060 date "2004-01-01" @default.
• W2183622060 modified "2023-09-24" @default.
• W2183622060 title "Ensemble Dependence Model for Classification of Cancer and Normal Patterns Using Gene Expression Data" @default.
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