Verwandte Artikel zu Mining Omics Data: From Correlation to Independence
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Biological studies across all omics fields generate vast amounts of data. To understand these complex data, biologically motivated data mining techniques are indispensable. In this dissertation, biological problems were individually addressed by using solutions from computational sciences. The interactome study identified graph clusters: A parameter-free graph-clustering algorithm was developed using graph compression to find highly interlinked proteins sharing similar characteristics. The lipidome study analyzed co-regulations: To reveal those lipids similarly responding to biological factors, a differential Gaussian Graphical Model was introduced accounting for disease-specificity. The cytome study of single cells aimed at understanding cellular systems: A novel noise robust source separation technique reliably extracted independent components from images describing protein behaviors. The study of peptides required the detection outstanding observations: By assessing regularities, an outlier detection algorithm was based on compression efficacy of independent components. All algorithms fulfilled diverse constraints, but were met with standard correlation and dependency methods.
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Über die Autorin bzw. den Autor
Nikola Müller studied Bioinformatics in Munich, Germany, and performed her doctoral work in bioinformatics at the Max Planck Institute of Biochemistry. She received her PhD at the Institute for Informatics at the Ludwig-Maximilians-University Munich.
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Mining Omics Data
Verlag:
Südwestdeutscher Verlag Für Hochschulschriften Jan 2013, 2013
ISBN 10: 3838135059
ISBN 13: 9783838135052
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Biological studies across all omics fields generate vast amounts of data. To understand these complex data, biologically motivated data mining techniques are indispensable. In this dissertation, biological problems were individually addressed by using solutions from computational sciences. The interactome study identified graph clusters: A parameter-free graph-clustering algorithm was developed using graph compression to find highly interlinked proteins sharing similar characteristics. The lipidome study analyzed co-regulations: To reveal those lipids similarly responding to biological factors, a differential Gaussian Graphical Model was introduced accounting for disease-specificity. The cytome study of single cells aimed at understanding cellular systems: A novel noise robust source separation technique reliably extracted independent components from images describing protein behaviors. The study of peptides required the detection outstanding observations: By assessing regularities, an outlier detection algorithm was based on compression efficacy of independent components. All algorithms fulfilled diverse constraints, but were met with standard correlation and dependency methods. 220 pp. Englisch. Bestandsnummer des Verkäufers 9783838135052
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Mining Omics Data
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mueller NikolaNikola Mueller studied Bioinformatics in Munich, Germany, and performed her doctoral work in bioinformatics at the Max Planck Institute of Biochemistry. She received her PhD at the Institute for Informatics at the Ludwig-. Bestandsnummer des Verkäufers 5407777
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Mining Omics Data : From Correlation to Independence
Verlag:
Südwestdeutscher Verlag Für Hochschulschriften, 2013
ISBN 10: 3838135059
ISBN 13: 9783838135052
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Biological studies across all omics fields generate vast amounts of data. To understand these complex data, biologically motivated data mining techniques are indispensable. In this dissertation, biological problems were individually addressed by using solutions from computational sciences. The interactome study identified graph clusters: A parameter-free graph-clustering algorithm was developed using graph compression to find highly interlinked proteins sharing similar characteristics. The lipidome study analyzed co-regulations: To reveal those lipids similarly responding to biological factors, a differential Gaussian Graphical Model was introduced accounting for disease-specificity. The cytome study of single cells aimed at understanding cellular systems: A novel noise robust source separation technique reliably extracted independent components from images describing protein behaviors. The study of peptides required the detection outstanding observations: By assessing regularities, an outlier detection algorithm was based on compression efficacy of independent components. All algorithms fulfilled diverse constraints, but were met with standard correlation and dependency methods. Bestandsnummer des Verkäufers 9783838135052
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Mining Omics Data | From Correlation to Independence
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Südwestdeutscher Verlag für Hochschulschriften, 2015
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Taschenbuch. Zustand: Neu. Mining Omics Data | From Correlation to Independence | Nikola Müller | Taschenbuch | 220 S. | Englisch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838135052 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 106099561
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Mining Omics Data
Verlag:
Südwestdeutscher Verlag Für Hochschulschriften Jan 2013, 2013
ISBN 10: 3838135059
ISBN 13: 9783838135052
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Biological studies across all omics fields generate vast amounts of data. To understand these complex data, biologically motivated data mining techniques are indispensable. In this dissertation, biological problems were individually addressed by using solutions from computational sciences. The interactome study identified graph clusters: A parameter-free graph-clustering algorithm was developed using graph compression to find highly interlinked proteins sharing similar characteristics. The lipidome study analyzed co-regulations: To reveal those lipids similarly responding to biological factors, a differential Gaussian Graphical Model was introduced accounting for disease-specificity. The cytome study of single cells aimed at understanding cellular systems: A novel noise robust source separation technique reliably extracted independent components from images describing protein behaviors. The study of peptides required the detection outstanding observations: By assessing regularities, an outlier detection algorithm was based on compression efficacy of independent components. All algorithms fulfilled diverse constraints, but were met with standard correlation and dependency methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 220 pp. Englisch. Bestandsnummer des Verkäufers 9783838135052
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