Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy

Oshrit Shtossel, Haim Isakov, Sondra Turjeman, Omry Koren, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics. However, several properties of microbial sequence-based studies hinder machine learning (ML), including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of taxa present in a small subset of samples. We show here using a graph representation that the cladogram structure is as informative as the taxa frequency. We then suggest a novel method to combine information from different taxa and improve data representation for ML using microbial taxonomy. iMic (image microbiome) translates the microbiome to images through an iterative ordering scheme, and applies convolutional neural networks to the resulting image. We show that iMic has a higher precision in static microbiome gene sequence-based ML than state-of-the-art methods. iMic also facilitates the interpretation of the classifiers through an explainable artificial intelligence (AI) algorithm to iMic to detect taxa relevant to each condition. iMic is then extended to dynamic microbiome samples by translating them to movies.

Original languageEnglish
Article number2224474
JournalGut Microbes
Issue number1
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.


  • 16S
  • CNN
  • GCN
  • Hierarchical ordering
  • machine learning
  • microbiome
  • taxonomy


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