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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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Bayesian models for two-sample time-course microarray experiments
TL;DR: The proposed procedure successfully deals with various technical difficulties which arise in microarray time-course experiments, such as small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements, thus offering a good compromise between nonparametric and normality assumption based techniques.
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TL;DR: This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain.
Estimating the prevalence of osteoporosis using ranked-based methodologies and Manitoba's population-based BMD registry
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TL;DR: The prevalence of osteoporosis among women of age 50 and older in Manitoba, Canada, can be accurately estimated using ranked-based methodologies.
Bayesian approaches to variable selection in mixture models with application to disease clustering
Zihang Lu,Wendy Lou +1 more
TL;DR: In biomedical research, cluster analysis is often performed to identify patient subgroups based on patients' characteristics or traits as mentioned in this paper, which is called model-based clustering for identifying patient sub groups.
Clustering time-course microarray data using functional Bayesian infinite mixture model
TL;DR: This paper presents a new Bayesian, infinite mixture model based, clustering approach, specifically designed for time-course microarray data, which is studied using synthetic and real micro array data and is compared with the performances of competitive techniques.