Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks | Zendy

Naohiro Kobayashi | Zendy; Yoshikazu Hattori | Zendy; Takashi Nagata | Zendy; Shoko Shinya | Zendy; Peter Güntert | Zendy; Chojiro Kojima | Zendy; Toshimichi Fujiwara | Zendy

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Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks

Author(s) -

Naohiro Kobayashi,

Yoshikazu Hattori,

Takashi Nagata,

Shoko Shinya,

Peter Güntert,

Chojiro Kojima,

Toshimichi Fujiwara

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty581

Subject(s) - computer science , noise (video) , nmr spectra database , spectral line , convolutional neural network , signal to noise ratio (imaging) , robot , artificial intelligence , pattern recognition (psychology) , algorithm , biological system , physics , telecommunications , astronomy , image (mathematics) , biology

Multi-dimensional NMR spectra are generally used for NMR signal assignment and structure analysis. There are several programs that can achieve highly automated NMR signal assignments and structure analysis. On the other hand, NMR spectra tend to have a large number of noise peaks even for data acquired with good sample and machine conditions, and it is still difficult to eliminate these noise peaks.

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