Neural Networks in Automatic Diagnosis Malignant Brain Tumors.
National Distance Education University, Madrid, Madrid, Spain
DOI: 10.1007/BFb0100545 Conference: Engineering Applications of Bio-Inspired Artificial Neural Networks, International Work-Conference on Artificial and Natural Neural Networks, IWANN '99, Alicante, Spain, June 2-4, 1999, Proceedings, Volume II
Automatic Proton Nuclear Magnetic Resonance (1H NMR) spectral based diagnosis malignant brain tumors is analyzed, using Neural Networks for classifying purposes. The Pattern
Recognition has been split into its principal parts or subproblems, adapting each one of them to the special task of analyzing
1H NMR spectra. We study the principal algorithms needed for solving those problems, and finally a distributed object oriented
classifying system is proposed, which attempts to solve the principal problems mentioned in each section.
Available from: John P Shockcor
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ABSTRACT: Biofluid NMR spectroscopy is a powerful tool providing a comprehensive metabolic profile of the low molecular weight components in biofluids that reflect concentrations and fluxes of endogenous metabolites involved in key intermediary cellular pathways, thereby giving an indication of an organisms physiological or pathophysiological status . The interaction of pharmacological agents with cells and tissues can also be monitored using recently developed high resolution magic-angle spinning (HRMAS) NMR spectroscopic technology for biological matrices . However, recent developments in both spectrometer and software technology has resulted in improved capacity for sample handling, leading to a rapid growth in the size of toxicological spectral databases, and increased the complexity of the biological spectral data generated. Thus more emphasis has been placed on the need to develop improved automated procedures for data processing and interpretation. By harnessing chemometric tools for analysis of complex spectral data, the toxicological consequences of xenobiotic exposure can be evaluated efficiently on line. Automation of spectral processing procedures and the construction of mathematically based 'expert systems' for the prediction of drug-induced toxicity founded on IH NMR spectral profiles have now been achieved. Chemometric analysis of biological NMR spectra has provided the main analytical platform for metabonomic analysis, providing a systems approach to evaluating pathophysiological or genetic influences on the metabolic status of an organism . This technology is currently being given high-priority in the pharmaceutical industry with respect to development of efficient high throughput toxicity screening systems for lead candidate selection. In this article, we review the recent developments in metabonomics and consider their application in toxicological screening, disease diagnosis and functional genomics.
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