Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data
Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.
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adjusted Affymetrix alternative markers approach arrays biological interpretation biological samples biomarker discovery bootstrap training sets calculated class separation classification model clusters cross-validation data mining database defined detection call differentiated classes discriminant analysis discriminatory information discriminatory power discriminatory space distance estimate example exons expression level feature selection filtering frequent primary genes function gene expression data gene expression matrix hyperplane identified Informative Set input intensities large number layer learning algorithm linear linear discriminant analysis mass spectrometry methods modified bagging schema multivariate biomarker neuron node normality number of variables OOB samples optimal biomarker overfitting p-dimensional p-value peaks peptide percent perfect OOB classifiers performed primary expression patterns principal component probe set protein microarrays proteomic random forests represented SELDI-TOF self-organizing map sequence Set of Genes statistical subset supervised learning Support Vector Machines support vectors SVMs test set training data set training samples univariate unsupervised variance