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Mining Omics Data

 

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  • Product Description
 

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.

Product Specifications
SKU :COC65619
AuthorNikola Müller
LanguageEnglish
BindingPaperback
Number of Pages220
Publishing Year2013-01-15T00:00:00.000
ISBN9783838135052
Edition1 st
Book TypeComputer networking & communications
Country of ManufactureIndia
Product BrandSüdwestdeutscher Verlag für Hochschulschriften
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-07-08 00:00:00