Current trends in computing: hardware, software and nuclear magnetic resonance research

Levy, George C.

doi:10.1016/0263-7855(86)80020-0

Search citation statements

Order By: Relevance

Paper Sections

Select...

Computational Aspects Of Nmr1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

1988

1994

Publication Types

Select...

Article4

Book1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current trend is to perform data reduction away from the spectrometer using general-purpose commercial workstations. Levy [1986] reviews some of the computational aspects of NMR.…”

Section: Computational Aspects Of Nmrmentioning

confidence: 99%

Artificial intelligence and molecular biology

1994

Choice Reviews Online

View full text Add to dashboard Cite

“…The current trend is to perform data reduction away from the spectrometer using general-purpose commercial workstations. Levy [1986] reviews some of the computational aspects of NMR.…”

Section: Computational Aspects Of Nmrmentioning

confidence: 99%

Artificial intelligence and molecular biology

1994

Choice Reviews Online

View full text Add to dashboard Cite

Computer-Enhanced Nuclear Magnetic Resonance Spectroscopy

Levy¹

1990

Computer-Enhanced Analytical Spectroscopy

View full text Add to dashboard Cite

NMRES: An artificial intelligence expert system for quantification of cardiac metabolites from31phosphorus nuclear magnetic resonance spectroscopy

1993

View full text Add to dashboard Cite

The application of high-resolution 31Phosphorus Nuclear Magnetic Resonance (31P NMR) Spectroscopy in biology and medicine has provided new insights into biochemical processes and also a unique assessment of metabolites. However, accurate quantification of biological NMR spectra is frequently complicated by: (a) non-Lorentzian form of peak lineshapes, (b) contamination of peak signals by neighboring peaks, (c) presence of broad resonances, (d) low signal-to-noise ratios, and (e) poorly defined sloping baselines. Our objectives were to develop an expert system that captures and formalizes 31P NMR spectroscopists' expert knowledge, and to provide a reliable, efficient, and automated system for the interpretation of biological spectra. The NMR Expert System (NMRES) was written in the C and OPS5 programming languages and implemented on a Unix-based (Ultrix) mainframe system with XWindows bit-map graphics display. Expert knowledge was acquired from NMR spectroscopists and represented as production rules in the knowledge base. A heuristic weights method was employed to determine the confidence levels of potential peaks. Statistical and numerical methods were used to facilitate processing decisions. NMR spectra obtained from studies of ischemic neonatal and immature hearts were used to assess the performance of the expert system. The expert system performed signal extraction, noise treatment, resonance assignment, intracellular pH determination, and metabolite intensity quantitation in about 10 s per 4 KB (kilobyte) spectrum. The peak identification success rate was 98.2%. Peak areas and pH estimated by the expert system compared favorably with those determined by human experts. We conclude that the expert system has provided a framework for reliable and efficient quantification of complex biological 31P NMR spectra.

show abstract

Current trends in computing: hardware, software and nuclear magnetic resonance research

Cited by 5 publications

References 8 publications

Artificial intelligence and molecular biology

Artificial intelligence and molecular biology

Computer-Enhanced Nuclear Magnetic Resonance Spectroscopy

NMRES: An artificial intelligence expert system for quantification of cardiac metabolites from31phosphorus nuclear magnetic resonance spectroscopy

Contact Info

Product

Resources

About