Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

The Preterm Birth DREAM Community

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals.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.

Original languageEnglish
Article number101350
JournalCell Reports Medicine
Volume5
Issue number1
DOIs
StateAccepted/In press - 2023

Keywords

  • 16S harmonization
  • crowdsourced
  • DREAM challenge
  • machine learning
  • microbiome
  • predictive modeling
  • preterm birth
  • vaginal microbiome

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