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Erschienen in:
A Road Map for Computational Surgery: Challenges and Opportunities Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

6. A Statistical Framework for Biomarker Analysis and HR-MAS 2D Metabolite Identification

verfasst von : Akram Belghith, Christophe Collet, Jean-Paul Armspach

Erschienen in: Computational Surgery and Dual Training

Verlag: Springer New York

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Abstract

Metabolomics is an exponentially growing field of “omics” research concerned with the high throughout comparison, identification and quantification of large numbers of metabolites in biological system. This emergent science of metabolomics brings increasing promise to identify biomarker diseases that integrate biochemical changes in disease and predict human reaction to treatment. In this context, the 2D High Resolution Magic Angle Spinning (HR-MAS) Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as an ideal platform for studying metabolites of biopsies. In this study, we particularly focus on the 2D Heteronuclear Single Quantum Coherence (HSQC) NMR spectrum analysis. The metabolomic analysis requires comparison of metabolite profiles obtained from multiple replicates of samples exposed to different experimental conditions. What adds difficulty to automating this analysis process is that each peak of a given metabolite (a set of peaks with specified locations) can be shifted slightly from one sample to the next. In this study, we propose a new framework to detect and align simultaneously peaks representing different metabolites within a biopsy for metabonomic analysis. The method was validated on synthetic and real HSQC spectra.

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Vorheriges Kapitel Computational Modeling of Breast Conserving Surgery (BCS) Starting from MRI Imaging
Nächstes Kapitel Hardware and Performance Considerations for Computational Medicine
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Metadaten
Titel
A Statistical Framework for Biomarker Analysis and HR-MAS 2D Metabolite Identification
verfasst von
Akram Belghith
Christophe Collet
Jean-Paul Armspach
Copyright-Jahr
2014
Verlag
Springer New York
Buch
Computational Surgery and Dual Training

Print ISBN: 978-1-4614-8647-3

Electronic ISBN: 978-1-4614-8648-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-1-4614-8648-0_6

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