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原文連結
論文資訊
- 類型：已發表論文
- 日期：2011-12-01
摘要
Background: Multi-gene interactions likely play an important role in the development of complex phenotypes, and relationships between interacting genes pose a challenging 統計 problem in microarray analysis, since the genes involved in these interactions may not exhibit marginal differential expression. As a result, it is necessary to develop tools that can identify sets of interacting genes that discriminate phenotypes without requiring that the classification boundary between phenotypes be convex. Results: We describe an extension and application of a new unsupervised 統計 learning technique, known as the Partition Decoupling Method (PDM), to gene expression microarray data. This method may be used to classify samples based on multi-gene expression patterns and to identify pathways associate
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