Literature DB >> 26824632

Application of Market Basket Analysis for the Visualization of Transaction Data Based on Human Lifestyle and Spectroscopic Measurements.

Yuka Shiokawa1, Takuma Misawa1,2, Yasuhiro Date1,2, Jun Kikuchi1,2,3.   

Abstract

With the innovation of high-throughput metabolic profiling methods such as nuclear magnetic resonance (NMR), data mining techniques that can reveal valuable information from substantial data sets are constantly desired in this field. In particular, for the analytical assessment of various human lifestyles, advanced computational methods are ultimately needed. In this study, we applied market basket analysis, which is generally applied in social sciences such as marketing, and used transaction data derived from dietary intake information and urinary chemical data generated using NMR and inductively coupled plasma optical emission spectrometry measurements. The analysis revealed several relationships, such as fish diets with high trimethylamine N-oxide excretion and N-methylnicotinamide excreted at higher levels in the morning and produced from a protein that was consumed one day prior. Therefore, market basket analysis can be applied to metabolic profiling to effectively understand the relationships between metabolites and lifestyle.

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Year:  2016        PMID: 26824632     DOI: 10.1021/acs.analchem.5b04182

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  10 in total

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2.  FoodPro: A Web-Based Tool for Evaluating Covariance and Correlation NMR Spectra Associated with Food Processes.

Authors:  Eisuke Chikayama; Ryo Yamashina; Keiko Komatsu; Yuuri Tsuboi; Kenji Sakata; Jun Kikuchi; Yasuyo Sekiyama
Journal:  Metabolites       Date:  2016-10-19

3.  Trans-omics approaches used to characterise fish nutritional biorhythms in leopard coral grouper (Plectropomus leopardus).

Authors:  Miyuki Mekuchi; Kenji Sakata; Tomofumi Yamaguchi; Masahiko Koiso; Jun Kikuchi
Journal:  Sci Rep       Date:  2017-08-24       Impact factor: 4.379

4.  Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

Authors:  Yuka Shiokawa; Yasuhiro Date; Jun Kikuchi
Journal:  Sci Rep       Date:  2018-02-21       Impact factor: 4.379

5.  Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis.

Authors:  Reza Ferdousi; Ali Akbar Jamali; Reza Safdari
Journal:  Bioimpacts       Date:  2019-11-02

Review 6.  Biomarkers of meat and seafood intake: an extensive literature review.

Authors:  Cătălina Cuparencu; Giulia Praticó; Lieselot Y Hemeryck; Pedapati S C Sri Harsha; Stefania Noerman; Caroline Rombouts; Muyao Xi; Lynn Vanhaecke; Kati Hanhineva; Lorraine Brennan; Lars O Dragsted
Journal:  Genes Nutr       Date:  2019-12-30       Impact factor: 5.523

7.  Oral Pathobiont-Induced Changes in Gut Microbiota Aggravate the Pathology of Nonalcoholic Fatty Liver Disease in Mice.

Authors:  Kyoko Yamazaki; Tamotsu Kato; Yuuri Tsuboi; Eiji Miyauchi; Wataru Suda; Keisuke Sato; Mayuka Nakajima; Mai Yokoji-Takeuchi; Miki Yamada-Hara; Takahiro Tsuzuno; Aoi Matsugishi; Naoki Takahashi; Koichi Tabeta; Nobuaki Miura; Shujiro Okuda; Jun Kikuchi; Hiroshi Ohno; Kazuhisa Yamazaki
Journal:  Front Immunol       Date:  2021-10-11       Impact factor: 7.561

Review 8.  The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.

Authors:  Jun Kikuchi; Shunji Yamada
Journal:  RSC Adv       Date:  2021-09-13       Impact factor: 4.036

9.  Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization.

Authors:  Hideaki Shima; Shizuka Masuda; Yasuhiro Date; Amiu Shino; Yuuri Tsuboi; Mizuho Kajikawa; Yoshihiro Inoue; Taisei Kanamoto; Jun Kikuchi
Journal:  Nutrients       Date:  2017-12-01       Impact factor: 5.717

10.  Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem.

Authors:  Feifei Wei; Kenji Sakata; Taiga Asakura; Yasuhiro Date; Jun Kikuchi
Journal:  Sci Rep       Date:  2018-02-22       Impact factor: 4.379

  10 in total

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