title: Assessment of quantitative artificial neural network analysis in a metabolically dynamic ex vivo 31P NMR pig liver study.
abstract: Quantitative artificial neural network analysis for 1550 ex vivo 31P nuclear 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 < or = R < or = 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.
authors.affiliation: Royal Free Hospital and Medical School, Hampstead, United Kingdom.
authors.forename: M
authors.initials: M
authors.lastname: Ala-Korpela
authors.forename: K K
authors.initials: KK
authors.lastname: Changani
authors.forename: Y
authors.initials: Y
authors.lastname: Hiltunen
authors.forename: J D
authors.initials: JD
authors.lastname: Bell
authors.forename: B J
authors.initials: BJ
authors.lastname: Fuller
authors.forename: D J
authors.initials: DJ
authors.lastname: Bryant
authors.forename: S D
authors.initials: SD
authors.lastname: Taylor-Robinson
authors.forename: B R
authors.initials: BR
authors.lastname: Davidson
mesh_terms: D000818:Animals; D008099:Liver / Q000378:metabolism*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D016571:Neural Networks, Computer; D013552:Swine