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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
Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN
Julio Pérez-Sánchez,Patricia Jimeno-Sáez,Javier Senent-Aparicio,José María Díaz-Palmero,Juan de Dios Cabezas-Cerezo +4 more
TL;DR: In this paper, the authors investigated the performance of an artificial neural network (ANN) in burned area size prediction and to assess the evolution of future wildfires and the area concerned under climate change in southern Spain.
Reverse Monte Carlo reconstruction algorithm for discrete electron tomography based on HAADF‐STEM atom counting
TL;DR: An algorithm to obtain a three‐dimensional reconstruction of a single nanoparticle based on the method of atom counting is proposed and successfully performed using simulations of high‐angle‐annular‐dark‐field images from only three zone‐axis projections.
•Dissertation
Factor Mixture Models with Ordered Categorical Outcomes: The Mathematical Relation to Mixture Item Response Theory Models and a Comparison Of Maximum Likelihood and Bayesian Model Parameter Estimation Methods
Xiaodong Hou
- 01 Jan 2011
TL;DR: In this article, Hou et al. investigated the relation between FMMs and mixture item response theory (IRT) models and provided a formal proof of the mathematical equivalence between mixture graded-response models and FMMs with ordered categorical outcomes.
Using evidence of mixed populations to select variables for clustering very high-dimensional data
Yao-ban Chan,Peter Hall +1 more
TL;DR: A nonparametric approach to clustering very high-dimensional data, designed particularly for problems where the mixture nature of a population is expressed through multimodality of its density, shows that a technique based implicitly on mode testing can be particularly effective.
A multivariate finite mixture latent trajectory model with application to dementia studies
TL;DR: In this paper, a multivariate finite mixture latent trajectory model is proposed to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests.