Document Type
Article
Publication Date
1-16-2024
Department
Department of Biological Sciences
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 The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals.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.
Publication Title
Cell Reports Medicine
Recommended Citation
Golob, J.,
Oskotsky, T.,
Tang, A.,
Roldan, A.,
Chung, V.,
Ha, C.,
Kuntzleman, A.,
Bigcraft, I.,
Techtmann, S.,
&
et al.
(2024).
Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.
Cell Reports Medicine,
5(1).
http://doi.org/10.1016/j.xcrm.2023.101350
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/324
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2023 The Authors. Publisher’s version of record: https://doi.org/10.1016/j.xcrm.2023.101350