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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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A Rough Set Approach to Face Recognition
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TL;DR: A Thesis Submitted to the Faculty of Graduate Studies & Research in Partial Fullfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science, University of Regina.
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Large-scale simultaneous inference with applications to the detection of differential expression with microarray data
TL;DR: In this article, a simple two-component normal mixture framework is proposed to estimate the posterior probability that an individual gene is null, based on the value of the test statistic used to test the significance of each gene.
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Physical Limnology and Sediment Dynamics of Lago Argentino, the World's Largest Ice‐Contact Lake
M. Van Wyk de Vries
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TL;DR: In this paper , a detailed analysis of 47 sediment cores obtained throughout this lake basin, supplemented with remotely sensed data, is presented, highlighting the exceptional preservation of annual laminations within proglacial lakes, their potential for reconstructing past glacier changes, and their relevance for forecasting future glacier-lake interactions.