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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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Monitoring foreclosure rates with a spatially risk-adjusted Bernoulli CUSUM chart for concurrent observations
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Learning-to-Rank for Hybrid User Profiles
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•Dissertation
Scalable approaches for analysis of human genome-wide expression and genetic variation data
Gad Abraham
- 01 Jan 2012
TL;DR: A range of predictive models are discussed and it is demonstrated that such models are computationally feasible and can scale to large datasets, provide increased insight into the biological causes of disease, and for some diseases have high predictive performance, allowing high-confidence disease diagnosis to be made based on genetic data.