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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
A Drug Administration Decision Support System
Wenqi You,Alena Simalatsar,Nicolas Widmer,Giovanni De Micheli +3 more
- 04 Oct 2012
TL;DR: A Drug Administration Decision Support System (DADSS) based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS.
Single Image Realism Assessment and Recoloring by Color Compatibility
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Predicting Partisan Responsiveness: A Probabilistic Text Mining Time-Series Approach
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Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach
TL;DR: The results obtained with the proposed multi-objective optimization based on surrogate modeling for a conceptual rainfall-runoff model support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably.
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A data mining framework based on boundary-points for gene selection from DNA-microarrays: Pancreatic Ductal Adenocarcinoma as a case study
Juan Pablo Hernández Ramos,José A. Castellanos-Garzón,José A. Castellanos-Garzón,Juan F. De Paz,Juan M. Corchado,Juan M. Corchado +5 more
TL;DR: A novel hybrid framework based on data mining techniques and tuned to select gene subsets, which are meaningfully related to the target disease conducted in DNA-microarray experiments, which has resulted in a methodology that can be followed in the gene selection process from DNA- microarray data.
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