Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research
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Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth researchAutor(es)
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2024-01-16Cita bibliográfica
J. L. Golob et al., «Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research», Cell Reports Medicine, vol. 5, n.o 1, p. 101350, ene. 2024, doi: 10.1016/j.xcrm.2023.101350.
Resumo
[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 The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals.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.
Palabras chave
Preterm birth
Vaginal microbiome
Machine learning
Predictive modeling
Crowdsourced
Microbiome
16S harmonization
DREAM challenge
Vaginal microbiome
Machine learning
Predictive modeling
Crowdsourced
Microbiome
16S harmonization
DREAM challenge
Descrición
This research was carried out within the framework of the DREAM Community of Premature Births, of which UDC researchers Diego Fernández-Edreira and Carlos Fernández-Lozano, who have collaborated in the research, are members. Supplementary research data are available at https://www.cell.com/cms/10.1016/j.xcrm.2023.101350/attachment/e44bcada-f500-4f17-bc33-0ee5d39b3c4b/mmc1.pdf.
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Atribución 3.0 España
ISSN
2666-3791