Decoding gut microbiota by imaging analysis of fecal samples

Chikara Furusawa, Kumi Tanabe, Chiharu Ishii, Noriko Kagata, Masaru Tomita, Shinji Fukuda

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis.

Original languageEnglish
Article number103481
JournaliScience
Volume24
Issue number12
DOIs
Publication statusPublished - 2021 Dec 17

Keywords

  • Biochemistry methods
  • Microbiome

ASJC Scopus subject areas

  • General

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