R scripts and programs


Ref: Ito, K., Xu, X. and Kikuchi, J. "Improved prediction of carbonless NMR spectra by the machine learning of theoretical and fragment descriptors for environmental mixture analysis" (submitted)

Data Cleansing Tools for NMR

Ref: Yamada, S., Kurotani, A., Chikayama, E. and Kikuchi, J. "Signal Deconvolution and Noise Factor Analysis based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization" Int. J. Mol. Sci., 21(8), 2978 (2020).


Ref: Ito, K., Tsuboi, Y. and Kikuchi, J. "Spatial molecular-dynamically ordered NMR spectroscopy of intact bodies and heterogeneous systems" Commun. Chem., 3(1), 1-8 (2020).


Ref: Ito, K., Obuchi, Y., Chikayama, E., Date, Y. and Kikuchi, J. "Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals" Chem. Sci., 9, 8213–8220 (2018).


Ref: Date, Y. and Kikuchi, J. "Application of a deep neural network to metabolomics studies and its performance in determining important variables" Anal. Chem., 90, 1805-1810 (2018).


Ref: Shiokawa, Y., Date, Y. and Kikuchi, J. "Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet" Sci. Rep., 8, 3426 (2018).


Ref: Asakura, T., Date, Y. and Kikuchi, J. "Application of ensemble deep neural network to metabolomics studies" Anal. Chem. Acta, 1037, 230-236 (2018).

*Machine-learning_protocol for three-step integrated analysis

Ref: Oita, A., Tsuboi, Y., Date, Y., Oshima, T., Sakata, K., Yokoyama, A., Moriya, S. and Kikuchi, J. "Profiling physicochemical and planktonic features from discretely/continuously sampled surface water" Sci. Total Environ., 636, 12-19 (2018).


Ref: Ito, K., Tsutsumi, Y., Date, Y. and Kikuchi, J. "Fragment Assembly Approach Based on Graph/Network Theory with Quantum Chemistry Verifications for Assigning Multidimensional NMR Signals in Metabolite Mixtures" ACS Chem. Biol., 11, 1030-1038 (2016).