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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Patent
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Michael Alan Black,Jonathan Cebon,Parry John Guilford,Thomas John +3 more
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TL;DR: In this article, the use of genetic and protein markers for the prediction of the risk of progression of a cancer, such as melanoma, based on markers and signatures of markers.
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Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets
TL;DR: A new feature selection method based on three steps to detect class-specific biomarkers in case of high-dimensional data sets, which shows that, using the proposed feature selection procedure, the classification performances of a Support Vector Machine on the imbalanced data set reach a 82% whereas other methods do not exceed 73%.
Multivariate-bounded Gaussian mixture model with minimum message length criterion for model selection
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TL;DR: The results presented in the paper demonstrate the effectiveness of BGMM for clustering speech and image databases, code‐book generation through clustering for feature representation and model selection, and minimum message length criterion for model selection in data clustering using multivariate BGMM.
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Learning Process Termination Criteria
Boštjan Brumen,Marko Hölbl,Katja Harej Pulko,Tatjana Welzer,Marjan Hericko,Matjaž B. Jurič,Hannu Jaakkola +6 more
TL;DR: An approach for an early assessment of the extracted knowledge (classification models) in the terms of performance (accuracy) by detecting the point of convergence, i.e., where the classification model's performance does not improve any more even when adding more data items to the learning set.
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•Dissertation
Modèles markoviens et extensions pour la classification de données complexes
Juliette Blanchet
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TL;DR: In this article, the authors adopt a modelle markovien gaussien non-diagonal to solve the problem of classifying observations dites incompletes, i.e. observations of large dimensions.
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