Microbiome preterm birth DREAM challenge: crowdsourcing machine learning approaches to advance preterm birth research
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.
Date Issued
2024-01-16
Date Acceptance
2023-12-01
Citation
Cell Reports Medicine, 2024, 5 (1)
ISSN
2666-3791
Publisher
Elsevier
Journal / Book Title
Cell Reports Medicine
Volume
5
Issue
1
Copyright Statement
© 2023 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S2666379123005670
Publication Status
Published
Article Number
101350
Date Publish Online
2023-12-21