Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease

Patients with inflammatory bowel disease (IBD) 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...

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Bibliographic Details
Published in:	Frontiers in genetics Vol. 13; p. 784397
Main Authors:	Kubinski, Ryszard, Djamen-Kepaou, Jean-Yves, Zhanabaev, Timur, Hernandez-Garcia, Alex, Bauer, Stefan, Hildebrand, Falk, Korcsmaros, Tamas, Karam, Sani, Jantchou, Prévost, Kafi, Kamran, Martin, Ryan D
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 14-02-2022
Subjects:	batch effect reduction data normalization Genetics gut microbiome inflammatory bowel disease machine learning QIIME2 PICRUSt2 data normalization batch effect reduction gut microbiome QIIME2 machine learning inflammatory bowel disease
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Summary:	Patients with inflammatory bowel disease (IBD) 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. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics Reviewed by: Anna Heintz-Buschart, University of Amsterdam, Netherlands Edited by: Harinder Singh, J. Craig Venter Institute (Rockville), United States Shirong Liu, Genentech Inc., United States
ISSN:	1664-8021 1664-8021
DOI:	10.3389/fgene.2022.784397