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Authors: Alejandro Chinea 1 and José L. González Mora 2

Affiliations: 1 Facultad de Ciencias UNED, Spain ; 2 Facultad de Medicina ULL and Campus de Ciencias de la Salud, Spain

Keyword(s): NMR Spectroscopy, Clinical Diagnosis, Machine Learning Applications.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Pharmaceutical Applications ; Structural Bioinformatics ; Systems Biology

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 structu ral 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. (More)

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Paper citation in several formats:
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 (BIOSTEC 2013) - BIOINFORMATICS; ISBN 978-989-8565-35-8; ISSN 2184-4305, SciTePress, pages 212-222. DOI: 10.5220/0004321902120222

@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 (BIOSTEC 2013) - BIOINFORMATICS},
year={2013},
pages={212-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004321902120222},
isbn={978-989-8565-35-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2013) - BIOINFORMATICS
TI - Structural Analysis of Nuclear Magnetic Resonance Spectroscopy Data
SN - 978-989-8565-35-8
IS - 2184-4305
AU - Chinea, A.
AU - L. González Mora, J.
PY - 2013
SP - 212
EP - 222
DO - 10.5220/0004321902120222
PB - SciTePress

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