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Titolo:
ASSESSMENT OF QUANTITATIVE ARTIFICIAL NEURAL-NETWORK ANALYSIS IN A METABOLICALLY DYNAMIC EX-VIVO P-31 NMR PIG-LIVER STUDY
Autore:
ALAKORPELA M; CHANGANI KK; HILTUNEN Y; BELL JD; FULLER BJ; BRYANT DJ; TAYLORROBINSON SD; DAVIDSON BR;
Indirizzi:
UNIV KUOPIO,AI VIRTANEN INST MOL SCI,POB 1627 FIN-70211 KUOPIO FINLAND RAAHE INST COMP ENGN,PER BRAHE LAB RAAHE FINLAND HAMMERSMITH HOSP,ROYAL POSTGRAD MED SCH,ROBERT STEINER NMR UNIT LONDON ENGLAND HAMMERSMITH HOSP,ROYAL POSTGRAD MED SCH,DIV GASTROENTEROL LONDON ENGLAND ROYAL FREE HOSP & MED SCH,DEPT SURG HAMPSTEAD ENGLAND ROYAL FREE HOSP & MED SCH,LIVER TRANSPLANT GRP HAMPSTEAD ENGLAND
Titolo Testata:
Magnetic resonance in medicine
fascicolo: 5, volume: 38, anno: 1997,
pagine: 840 - 844
SICI:
0740-3194(1997)38:5<840:AOQANA>2.0.ZU;2-4
Fonte:
ISI
Lingua:
ENG
Soggetto:
HUMAN BLOOD-PLASMA; IN-VIVO; H-1-NMR DATA; CLASSIFICATION; SPECTRA; SPECTROSCOPY; QUANTIFICATION;
Keywords:
ARTIFICIAL NEURAL NETWORK ANALYSIS; EX VIVO P-31 NMR OF LIVER; QUANTIFICATION; AUTOMATIC DATA ANALYSIS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
18
Recensione:
Indirizzi per estratti:
Citazione:
M. Alakorpela et al., "ASSESSMENT OF QUANTITATIVE ARTIFICIAL NEURAL-NETWORK ANALYSIS IN A METABOLICALLY DYNAMIC EX-VIVO P-31 NMR PIG-LIVER STUDY", Magnetic resonance in medicine, 38(5), 1997, pp. 840-844

Abstract

Quantitative artificial neural network analysis for 1550 ex vivo P-31nuclear magnetic resonance spectra from hypothermically reperfused pig livers was assessed. These spectra show wide ranges of metabolite concentrations and have been analyzed using metabolite prior knowledge based lineshape fitting analysis which had proved robust in its biochemical interpretation. This finding provided a good opportunity to assess the performance of artificial neural network analysis in a biochemically complex situation. The results showed high correlations (0.865 less than or equal to R less than or equal to 0.992) between the lineshape fitting and artificial neural network analysis for the metabolite values, and the artificial neural network analysis was able to fully represent the trends in the metabolic fluctuations during the experiments.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 07/07/20 alle ore 14:46:05