Open AccessBook
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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Personalized Drug Administrations Using Support Vector Machine
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An enduring interest in classification: supervised and unsupervised
Geoffrey J. McLachlan
- 01 Jan 2012
TL;DR: The author has had an enduring interest in discriminant and cluster analyses, that is, in classification both supervised and unsupervised in fields such as artificial intelligence, machine learning, and pattern recognition.
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Naive Bayes for statlog heart database with consideration of data specifics
Jan Bohacik,Michal Zabovsky +1 more
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TL;DR: A discretization algorithm of numerical attributes which takes the specifics of given heart disease patients into account is presented and improvements of accuracy are measured with sensitivity, specificity and their sum and the results are compared with other classification algorithms.
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Pursuing sources of heterogeneity in modeling clustered population.
TL;DR: In this article, a regularized finite mixture effects regression was proposed to achieve heterogeneity pursuit and feature selection simultaneously, which can achieve both estimation and selection consistency in a heterogeneous population with mixed regression relationships.
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•Dissertation
Problem decomposition by mutual information and force-based clustering
Richard Edward Otero
- 28 Mar 2012
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