TY  - CONF
AU  - Chinea, A.
AU  - González-Mora, J.L.
T1  - Structural analysis of nuclear magnetic resonance spectroscopy data
LA  - eng
PY  - 2013
SP  - 212
EP  - 222
T2  - BIOINFORMATICS 2013 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms
SN  - 9789898565358
AB  - 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 highdimensional characteristics of NMR data but also with the dynamic aspects of these signals. Copyright © 2013 SCITEPRESS - Science and Technology Publications.
UR  - https://portalciencia.ull.es/documentos/5e39b79f2999523aa92714c5
DP  - Dialnet - Portal de la Investigación
ER  -