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: 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: 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