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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
Sparse principal components by semi-partition clustering
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Analyze of Different Algorithms of Machine Learning for Loan Approval
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Querying multiple sets of p-values through composed hypothesis testing.
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Identification of coenzyme-binding proteins with machine learning algorithms.
Yong Liu,Cristian R. Munteanu,Zhiwei Kong,Tao Ran,Alfredo Sahagún-Ruiz,Zhixiong He,Chuanshe Zhou,Zhiliang Tan +7 more
TL;DR: A Random Forest classifier based on 3 features of the embedded and non-embedded graphs was identified as the best predictive model for coenzyme-binding proteins.
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Interaction Visualisation of Complex Genomic Data with Game Engines
Nader H Khalifa,Quang Vinh Nguyen,Simeon J. Simoff,Daniel Catchpoole +3 more
- 01 Jul 2017
TL;DR: A visual analytics model is presented that enables the analysis of large and complex genomic data using Unity3D game technology and includes an interactive visualisation, providing an overview of the patient cohort with a detailed view of the individual genes.
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