NMR Informatics tools

References & Programs


Ref: Shima, H., Asakura, T., Sakata, K., Koiso,M. and Kikuchi, J. “Feed components and timing to improve feed conversion ratio for sustainable aquaculture using starch” Int. J. Mol. Sci. 25 14 (2024).

PGdata.csv↲


Ref: Yokoyama, D., Takamura, A., Tsuboi, Y. and Kikuchi, J. “Large-scale omics dataset of polymer degradation provides robust interpretation for microbial niche and succession on different plastisphere” ISME com. 3 67 (2023).

Data and R scripts for polymer microbiome analysis↲


Ref: Hara, K., Yamada, S., Kurotani, A., Chikayama, E., and Kikuchi, J. "Materials informatics approach using domain modelling for exploring structure-property relationships of polymers" Scientific Reports 12 10558 (2022).

MatRigiCa.zip↲


Ref: Yokoyama, D., Suzuki, S., Asakura, T., Kikuchi, J. "Chemometric analysis of NMR spectra and machine learning to investigate membrane fouling" ACS Omega 7 12654-12660 (2022).

R scripts for membrane fouling analysis↲


Ref: Miyamoto, H., ... and Kikuchi. "A potential network structure of symbiotic bacteria involved in carbon and nitrogen metabolism of wood-utilizing insect larvae" Sci. Tot. Env. 836 155520 (2022).

R script for Market Basket Analysis (MBA)↲


Ref: Yamawaki, R., Tei, A., Ito, K. and Kikuchi, J. "Decomposition Factor Analysis Based on Virtual Experiments Throughout Bayesian Optimization for Compost-Degradable Polymers" Appl. Sci. 11, 2820 (2021).

Virtual_expt_program.zip↲
READ_ME↲


Ref: Yamada, S., Chikayama, E. and Kikuchi, J. "Signal Deconvolution and Generative Topographic Mapping Regression for Solid-state NMR of Multi-component Materials" Int. J. Mol. Sci. 22, 1086 (2021).

ssNMR-SignalDeconvolution-GTMR.zip↲


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" Anal. Chem. 93, 6901-6906 (2021).

Predict_2DJ_programs_and_data.zip↲
READ_ME↲


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, 2978 (2020).

Data Cleansing Tools for NMR ver20200331.zip↲
SIforDCTN.zip↲
READ_ME↲


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

SMOOSY_processor.zip↲
READ_ME↲


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).

Predict_CS_programs_and_data.zip↲
READ_ME↲
ML_dataset.zip↲
About_ML_dataset↲


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).

DNN-MDA_algorithm↲


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).

KPCA_protocol↲
cforest_protocol↲
MBA_protocol↲


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

EDNN_protocol↲


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).

Machine-learning_protocol for three-step integrated analysis↲
Factor-mapping_protocol↲
FEVD_protocol↲


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).

FAA.zip↲
READ_ME↲


Contact

RIKEN Center for Sustainable Resource Science
Environmental Metabolic Analysis Research Team
Jun KIKUCHI Ph.D.

Email:jun.kikuchi at riken.jp
1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan