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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
Improving Label-Free Quantitative Proteomics Strategies by Distributing Shared Peptides and Stabilizing Variance
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Adaptable image segmentation via simple pixel classification
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Perspectives: Gene expression in fisheries management
TL;DR: Current genomic research in fisheries focusing on those that use microarrays to explore differences in gene expression among phenotypes and within or across populations, information that is critically important to the conservation of fish and their relationship to humans are reviewed.
Identifying subgroups of age and cohort effects in obesity prevalence
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