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Analyzing Microarray Gene Expression Data
Geoffrey J. McLachlan,Kim Anh Do,Christophe Ambroise +2 more
- 04 Aug 2004
875
TL;DR: In this article, the authors proposed a supervised classification of Tissue Samples and linked the supervised classification with survival analysis, and showed that the classification of tissue samples is more accurate than that of microarray data.
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Abstract: Preface. 1. Microarrays in Gene Expression Studies. 2. Cleaning and Normalization. 3. Some Cluster Analysis Methods. 4. Clustering of Tissue Samples. 5. Screening and Clustering of Genes. 6. Discriminant Analysis. 7. Supervised Classification of Tissue Samples. 8. Linking Microarray Data with Survival Analysis. References. Author Index. Subject Index.
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Automated analysis of internally programmed grooming behavior in Drosophila using a k-nearest neighbors classifier
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Identification of K-Tolerance Regulatory Modules in Time Series Gene Expression Data Using a Biclustering Algorithm
Tustanah Phukhachee,Songrit Maneewongvatana +1 more
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TL;DR: This work proposes a suffix tree based algorithm that allows biclusters to have inconsistencies in at most k contiguous column, which can reveals previously undiscoverable bicLusters.
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Identification of target clusters by using the restricted normal mixture model
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