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Open AccessArticle

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

1
The Laboratory of Microbiology, Showa Pharmaceutical University, Machida, Tokyo 194-8543, Japan
2
RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
3
Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
4
Laboratory of Pharmaceutical Sciences and Education, Showa Pharmaceutical University, Machida, Tokyo 194-8543, Japan
5
Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2017, 9(12), 1307; https://doi.org/10.3390/nu9121307
Received: 30 September 2017 / Revised: 13 November 2017 / Accepted: 15 November 2017 / Published: 1 December 2017
(This article belongs to the Special Issue Prebiotics and Probiotics)
Prebiotics and probiotics strongly impact the gut ecosystem by changing the composition and/or metabolism of the microbiota to improve the health of the host. However, the composition of the microbiota constantly changes due to the intake of daily diet. This shift in the microbiota composition has a considerable impact; however, non-pre/probiotic foods that have a low impact are ignored because of the lack of a highly sensitive evaluation method. We performed comprehensive acquisition of data using existing measurements (nuclear magnetic resonance, next-generation DNA sequencing, and inductively coupled plasma-optical emission spectroscopy) and analyses based on a combination of machine learning and network visualization, which extracted important factors by the Random Forest approach, and applied these factors to a network module. We used two pteridophytes, Pteridium aquilinum and Matteuccia struthiopteris, for the representative daily diet. This novel analytical method could detect the impact of a small but significant shift associated with Matteuccia struthiopteris but not Pteridium aquilinum intake, using the functional network module. In this study, we proposed a novel method that is useful to explore a new valuable food to improve the health of the host as pre/probiotics. View Full-Text
Keywords: gut ecosystem; food intake; metabolic response; machine learning; network analysis gut ecosystem; food intake; metabolic response; machine learning; network analysis
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Shima, H.; Masuda, S.; Date, Y.; Shino, A.; Tsuboi, Y.; Kajikawa, M.; Inoue, Y.; Kanamoto, T.; Kikuchi, J. Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization. Nutrients 2017, 9, 1307.

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