Structural Analysis of Nuclear Magnetic Resonance Spectroscopy Data

Alejandro Chinea, José L. González Mora

2013

Abstract

From the clinical diagnosis point of view in vivo nuclear magnetic resonance (NMR) spectroscopy has proven to be a valuable tool for performing non-invasive quantitative assessments of brain tumour glucose metabolism. Brain tumours are considered fast-growth tumours because of their high rate of proliferation. Therefore, there is strong interest from the clinical investigator’s point of view in the development of early tumour detection techniques. Unfortunately, current diagnosis techniques ignore the dynamic aspects of these signals. It is largely believed that temporal variations of NMR spectra are simply due to noise or do not carry enough information to be exploited by any reliable diagnosis procedure. Thus, current diagnosis procedures are mainly based on empirical observations extracted from single averaged spectra. In this paper, a machine learning framework for the analysis of NMR spectroscopy signals is introduced. The proposed framework is characterized by a set of structural parameters that are shown to be very sensitive to metabolic changes as those exhibited by tumour cells. Furthermore, they are able to cope not only with high-dimensional characteristics of NMR data but also with the dynamic aspects of these signals.

Paper Citation

in Harvard Style

Chinea A. and L. González Mora J. (2013). Structural Analysis of Nuclear Magnetic Resonance Spectroscopy Data . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013) ISBN 978-989-8565-35-8, pages 212-222. DOI: 10.5220/0004321902120222

in Bibtex Style

@conference{bioinformatics13,
author={Alejandro Chinea and José L. González Mora},
title={Structural Analysis of Nuclear Magnetic Resonance Spectroscopy Data},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)},
year={2013},
pages={212-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004321902120222},
isbn={978-989-8565-35-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)
TI - Structural Analysis of Nuclear Magnetic Resonance Spectroscopy Data
SN - 978-989-8565-35-8
AU - Chinea A.
AU - L. González Mora J.
PY - 2013
SP - 212
EP - 222
DO - 10.5220/0004321902120222