Neural networks for the peak-picking of nuclear magnetic resonance spectra
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Cited by (30)
Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing
2013, Genomics, Proteomics and BioinformaticsCitation Excerpt :Expected properties of peak shapes, such as the symmetry property, were used to identify peaks. Since then, a variety of computational methods have been utilized, including peak-property-based methods [11,12], machine learning methods [13–16], and spectra-decomposition-based methods [17–19]. Recently, image processing techniques have been applied to the peak picking problem and they have demonstrated promising performance [20,21].
Automated Peak Picking and Peak Integration in Macromolecular NMR Spectra Using AUTOPSY
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1998, Expert Systems with ApplicationsChapter 19 Neural networks for 2D NMR spectroscopy
1996, Data Handling in Science and TechnologyFrom neural chip and engineered biomolecules to bioelectronic devices: An overview
1995, Biosensors and Bioelectronics
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