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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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Citations
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The Noisy Expectation-Maximization Algorithm for Multiplicative Noise Injection
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Feasibility and Duality
Kenneth Lange
- 01 Jan 2013
TL;DR: In this article, the authors provide a concrete introduction to several advanced topics in optimization theory. But they do not address the problem of finding an interior feasible point, which is the first issue that must be faced in applying a barrier method.
Clustering high-throughput sequencing data with Poisson mixture models
Andrea Rau,Gilles Celeux,Marie-Laure Martin-Magniette,Cathy Maugis-Rabusseau,Cathy Maugis-Rabusseau +4 more
- 03 Nov 2011
TL;DR: This work proposes two parameterizations of a Poisson mixture model to cluster expression profiles of high-throughput sequencing data and compares the performance of these approaches with that of an approach developed for a similar type of data, namely serial analysis of gene expression.