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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
Do High Consumers of Sugar-Sweetened Beverages Respond Differently to Price Changes? A Finite Mixture IV-Tobit Approach.
Fabrice Etilé,Anurag Sharma +1 more
TL;DR: The authors compared the impact of sugar-sweetened beverages (SSBs) tax between moderate and high consumers in Australia and found that high consumers of SSBs have a less elastic demand for SSBs, their very high consumption levels imply that a tax would achieve higher reduction in consumption and higher health gains.
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A machine learning approach for predicting bank credit worthiness
Regina Esi Turkson,Edward Yeallakuor Baagyere,Gideon Evans Wenya +2 more
- 01 Sep 2016
TL;DR: A predictive model is formulated using the most important features to predict the credit worthiness of a given customer using several machine learning algorithms.
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Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources.
TL;DR: The presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions and has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.
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geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research.
TL;DR: The geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research.
Subtyping of children with developmental dyslexia via bootstrap aggregated clustering and the gap statistic: comparison with the double-deficit hypothesis
TL;DR: This study finds three subtypes predicted by the double-deficit hypothesis without the assumption of an a priori theoretical model of reading in children with developmental dyslexia.
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