Könyv Mining Omics Data Nikola Müller

Mining Omics Data

From Correlation to Independence

Szerző: Nikola Müller
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
30 758 Ft
Biological studies across all omics fields generate vast amounts of data. To understand these comple...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2013
oldal
220
EAN
9783838135052
ISBN
3838135059
Enbook ID
06958183
Súly
327
Méretek
152 x 229 x 13

Teljes leírás

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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