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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Citations
Predictive Models for Differentiation Between Normal and Abnormal EEG Through Cross-Correlation and Machine Learning Techniques
Jefferson Tales Oliva,João Luís Garcia Rosa +1 more
- 01 Jan 2017
TL;DR: P predictive models were built using machine learning algorithms such as J48, 1NN, and BP-MLP (backpropagation based on multilayer perceptron), that implement decision tree, nearest neighbor, and artificial neural network, respectively, and showed that the model built with the J48 performed better and was more likely to correctly classify EEG segments in this study, corresponding to 98.50% accuracy.
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Interpretable and fast dimension reduction of multivariate data
Doyo Gragn Enki
- 01 Jan 2011
TL;DR: The resulting sparse components are found to be more interpretable and explain higher cumulative percentage of adjusted variance compared to their counterparts from other techniques, and contribute much to the interpretation of components in a reduced dimension while dealing with dimensionality reduction of multivariate data.
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An Algorithmic Approach to Personalized Drug Concentration Predictions
Wenqi You Dubout
- 01 Jan 2014
TL;DR: In this paper, the authors presented a Support Vector Machine (SVM) for Drug Administration Decision Support System (DSS) at the Ecole polytechnique federale de Lausanne EPFL.
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Optimal flow analysis, prediction and application.
TL;DR: This work is the first work that employs statistical learning technique to analyze the optimal flow of the fixed charge network flow (FCNF) problem and identifies 26 statistical significant predictors for logistic regression to predict which arcs will have positive flow in the optimal solutions.
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Challenges and Outlook in Machine Learning-based Malware Detection for Android
Kevin Allix
- 09 Oct 2015
TL;DR: This thesis investigates issues that affect performance evaluation and that thus may render current machine learning-based malware detectors for Android hardly usable in practical settings, and proposes an approach to overcome those issues.
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