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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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TL;DR: A linear discriminant analysis based classifier is used, and the extraction of time-frequency domain features through the use of the wavelet transform is discussed, and a weighted multi-classifier combination method for combining outputs of multiple classifiers into a single coherent output is implemented.
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Predictor selection method for the construction of support vector machine (SVM)-based typhoon rainfall forecasting models using a non-dominated sorting genetic algorithm
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Partition clustering of high dimensional low sample size data based on p-values
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What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review
André Victória Matias,João Gustavo Atkinson Amorim,Luiz Antonio Buschetto Macarini,Allan Cerentini,Alexandre Sherlley Casimiro Onofre,Fabiana Botelho de Miranda Onofre,Felipe Perozzo Daltoé,Marcelo Ricardo Stemmer,Aldo von Wangenheim +8 more
TL;DR: In this article, the authors conducted a systematic literature review to identify the state-of-the-art of computer vision techniques currently applied to cytology, and found that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers).
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Computational approaches for the analysis of RNA-protein interactions: A primer for biologists
Kat S. Moore,Peter A C 't Hoen +1 more
TL;DR: This review discusses statistical inference and machine-learning approaches and tools relevant for the study of RBPs and the analysis of large-scale RNA–protein interaction datasets, and begins with the demystification of regression models, as used in theAnalysis of next-generation sequencing data.
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