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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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From medical images to multiple-biomarker microarrays.
TL;DR: Some historical difficulties in predicting the future of diagnostic medical imaging are recalled, and the analogous situation in the emerging technology of diagnostic microarrays for the multiple-biomarker problem is examined.
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Modelling production uncertainties using the adaptive neuro-fuzzy inference system
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Systematic Testing of the Data-Poisoning Robustness of KNN
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- 12 Jul 2023
TL;DR: In this paper , the authors proposed a systematic testing based method, which can falsify as well as certify data-poisoning robustness for a widely used supervised learning technique named k-nearest neighbors (KNN).
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•Dissertation
Intelligent Condition Assessment of Power Transformer Based on Data Mining Techniques
Monsef Tahir
- 18 Jan 2013
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Bootstrap likelihood ratio test for Weibull mixture models fitted to grouped data
Youjiao Yu,Jane L. Harvill +1 more
TL;DR: In this paper, the authors focus on circumstances in which original observations are not available, and use Weibull mixture models to model phenomena caused by heterogeneous sources, which are widely used in a variety of fields.
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