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Visual data mining in intrinsic hierarchical complex biodata: Novel approaches for analyzing gene expression data in biomedicine and sequence data in metagenomics - Softcover
Synopsis
Complex biological data is characterized by a high dimensionality, multi-modality, missing values and noisiness, making its analysis a challenging task. Complex data consists of primary data - the core data - produced by a modern high-throughput technology, and secondary data, a collection of all kinds of respective supplementary data and background knowledge. Furthermore, biological data often has an intrinsic hierarchical structure, e.g. species in the Tree of Life. In this book, novel visual data mining approaches for the analysis of gene expression data in biomedicine and for sequence data in metagenomics are presented. To support the analysis of gene expression data, a Tree Index is developed for external validation of hierarchical clustering results and for correlation analysis between clustered primary data and external labels. To support visual inspection of the data, the REEFSOM – a metaphoric data display - is adapted to integrate clustered gene expression data, clinical data and categorical data in one display. In the domain of metagenomics, a Self-Organizing Map classifier is developed in hyperbolic space to classify small variable-length DNA fragments.
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About the Author
Christian Wolfgang Martin studied computer science at theUniversities of Bielefeld and Geneva and graduated in 2003. Hereceived a scholarship of the International Graduate SchoolBioinformatics and Genome Research and obtained a Ph.D. inbioinformatics in 2009. His main interests are focused on machinelearning methods applied in biomedicine.
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Visual data mining in intrinsic hierarchical complex biodata
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Südwestdeutscher Verlag Für Hochschulschriften Aug 2009, 2009
ISBN 10: 3838109791
ISBN 13: 9783838109794
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Complex biological data is characterized by a high dimensionality, multi-modality, missing values and noisiness, making its analysis a challenging task. Complex data consists of primary data - the core data - produced by a modern high-throughput technology, and secondary data, a collection of all kinds of respective supplementary data and background knowledge. Furthermore, biological data often has an intrinsic hierarchical structure, e.g. species in the Tree of Life. In this book, novel visual data mining approaches for the analysis of gene expression data in biomedicine and for sequence data in metagenomics are presented. To support the analysis of gene expression data, a Tree Index is developed for external validation of hierarchical clustering results and for correlation analysis between clustered primary data and external labels. To support visual inspection of the data, the REEFSOM - a metaphoric data display - is adapted to integrate clustered gene expression data, clinical data and categorical data in one display. In the domain of metagenomics, a Self-Organizing Map classifier is developed in hyperbolic space to classify small variable-length DNA fragments. 156 pp. Deutsch. Seller Inventory # 9783838109794
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Visual data mining in intrinsic hierarchical complex biodata : Novel approaches for analyzing gene expression data in biomedicine and sequence data in metagenomics
Published by
Südwestdeutscher Verlag Für Hochschulschriften, 2009
ISBN 10: 3838109791
ISBN 13: 9783838109794
New
Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Complex biological data is characterized by a high dimensionality, multi-modality, missing values and noisiness, making its analysis a challenging task. Complex data consists of primary data - the core data - produced by a modern high-throughput technology, and secondary data, a collection of all kinds of respective supplementary data and background knowledge. Furthermore, biological data often has an intrinsic hierarchical structure, e.g. species in the Tree of Life. In this book, novel visual data mining approaches for the analysis of gene expression data in biomedicine and for sequence data in metagenomics are presented. To support the analysis of gene expression data, a Tree Index is developed for external validation of hierarchical clustering results and for correlation analysis between clustered primary data and external labels. To support visual inspection of the data, the REEFSOM - a metaphoric data display - is adapted to integrate clustered gene expression data, clinical data and categorical data in one display. In the domain of metagenomics, a Self-Organizing Map classifier is developed in hyperbolic space to classify small variable-length DNA fragments. Seller Inventory # 9783838109794
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Visual data mining in intrinsic hierarchical complex biodata
Published by
Südwestdeutscher Verlag Für Hochschulschriften Aug 2009, 2009
ISBN 10: 3838109791
ISBN 13: 9783838109794
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Complex biological data is characterized by a high dimensionality, multi-modality, missing values and noisiness, making its analysis a challenging task. Complex data consists of primary data - the core data - produced by a modern high-throughput technology, and secondary data, a collection of all kinds of respective supplementary data and background knowledge. Furthermore, biological data often has an intrinsic hierarchical structure, e.g. species in the Tree of Life. In this book, novel visual data mining approaches for the analysis of gene expression data in biomedicine and for sequence data in metagenomics are presented. To support the analysis of gene expression data, a Tree Index is developed for external validation of hierarchical clustering results and for correlation analysis between clustered primary data and external labels. To support visual inspection of the data, the REEFSOM ¿ a metaphoric data display - is adapted to integrate clustered gene expression data, clinical data and categorical data in one display. In the domain of metagenomics, a Self-Organizing Map classifier is developed in hyperbolic space to classify small variable-length DNA fragments.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Deutsch. Seller Inventory # 9783838109794
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Visual data mining in intrinsic hierarchical complex biodata | Novel approaches for analyzing gene expression data in biomedicine and sequence data in metagenomics
Published by
Südwestdeutscher Verlag für Hochschulschriften, 2009
ISBN 10: 3838109791
ISBN 13: 9783838109794
New
Taschenbuch
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Taschenbuch. Condition: Neu. Visual data mining in intrinsic hierarchical complex biodata | Novel approaches for analyzing gene expression data in biomedicine and sequence data in metagenomics | Christian W. Martin | Taschenbuch | Paperback | 156 S. | Deutsch | 2009 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838109794 | Verantwortliche Person für die EU: Südwestdt. Verl. f. Hochschulschrift., Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 101491412
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