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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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Prediction of the Need for Orthognathic Surgery in Patients With Cleft Lip and/or Palate.
TL;DR: Ten cephalometric variables might be regarded as effective predictors of the future need for surgery to correct their sagittal skeletal discrepancies in Korean male patients with nonsyndromic cleft lip and alveolus and unilateral patients with unilateral cleftlip and palate.
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Tail Posterior Probability for Inference in Pairwise and Multiclass Gene Expression Data
TL;DR: A new rule is introduced, tail posterior probability, based on the posterior distribution of the standardized difference, to identify genes differentially expressed between two conditions, and a frequentist estimator of the false discovery rate associated with this rule is derived.
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