Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease

Ryszard Kubinski, Jean-Yves Djamen-Kepaou, Timur Zhanabaev, Alex Hernandez-Garcia, Stefan Bauer, Falk Hildebrand, View ORCID ProfileTamas Korcsmaros, Sani Karam, Prévost Jantchou, Kamran Kafi, Ryan D. Martin
doi: https://doi.org/10.1101/2021.05.03.442488
Ryszard Kubinski
1Phyla Technologies Inc, Montreal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: richard@phyla.ai ryan.martin@phyla.ai
Jean-Yves Djamen-Kepaou
1Phyla Technologies Inc, Montreal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Timur Zhanabaev
1Phyla Technologies Inc, Montreal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alex Hernandez-Garcia
2Mila, Quebec Artificial Intelligence Institute, University of Montreal, Montreal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stefan Bauer
3Max Planck Institute for Intelligent Systems, Tübingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Falk Hildebrand
4Gut Microbes & Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK
5Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tamas Korcsmaros
4Gut Microbes & Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK
5Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tamas Korcsmaros
Sani Karam
1Phyla Technologies Inc, Montreal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Prévost Jantchou
6Centre Hospitalier Universitaire Sainte-Justine, Montréal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kamran Kafi
1Phyla Technologies Inc, Montreal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ryan D. Martin
1Phyla Technologies Inc, Montreal, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: richard@phyla.ai ryan.martin@phyla.ai
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Background Inflammatory bowel disease (IBD) patients wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome’s composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools.

Results We collected fifteen 16S rRNA microbiome datasets (7707 samples) from North America to benchmark combinations of gut microbiome features, data normalization methods, batch effect reduction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with four binary classification metrics following leave-one dataset-out cross validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features obtained from QIIME2 lead to better classification of samples from IBD patients than inferred functional features obtained from PICRUSt2. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Prior to training a non-linear machine learning model on taxonomic features, it is important to apply a compositional normalization method and remove batch effects with the naive zero-centering method. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics.

Conclusions These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

Competing Interest Statement

RK is a founder of Phyla Technologies Inc and is currently the Chief Scientific Officer. RM, JD, and TZ were employed by Phyla Technologies Inc at the time of the manuscript.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted May 04, 2021.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease
Ryszard Kubinski, Jean-Yves Djamen-Kepaou, Timur Zhanabaev, Alex Hernandez-Garcia, Stefan Bauer, Falk Hildebrand, Tamas Korcsmaros, Sani Karam, Prévost Jantchou, Kamran Kafi, Ryan D. Martin
bioRxiv 2021.05.03.442488; doi: https://doi.org/10.1101/2021.05.03.442488
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease
Ryszard Kubinski, Jean-Yves Djamen-Kepaou, Timur Zhanabaev, Alex Hernandez-Garcia, Stefan Bauer, Falk Hildebrand, Tamas Korcsmaros, Sani Karam, Prévost Jantchou, Kamran Kafi, Ryan D. Martin
bioRxiv 2021.05.03.442488; doi: https://doi.org/10.1101/2021.05.03.442488

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Microbiology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4658)
  • Biochemistry (10313)
  • Bioengineering (7636)
  • Bioinformatics (26241)
  • Biophysics (13481)
  • Cancer Biology (10650)
  • Cell Biology (15363)
  • Clinical Trials (138)
  • Developmental Biology (8467)
  • Ecology (12776)
  • Epidemiology (2067)
  • Evolutionary Biology (16794)
  • Genetics (11373)
  • Genomics (15431)
  • Immunology (10580)
  • Microbiology (25087)
  • Molecular Biology (10172)
  • Neuroscience (54234)
  • Paleontology (398)
  • Pathology (1660)
  • Pharmacology and Toxicology (2884)
  • Physiology (4326)
  • Plant Biology (9213)
  • Scientific Communication and Education (1582)
  • Synthetic Biology (2545)
  • Systems Biology (6761)
  • Zoology (1459)