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Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research
dc.contributor.author | Golob, Jonathan L. | |
dc.contributor.author | Oskotsky, Tomiko T. | |
dc.contributor.author | Tang, Alice S. | |
dc.contributor.author | Roldan, Alennie | |
dc.contributor.author | Chung, Verena | |
dc.contributor.author | Ha, Connie W.Y. | |
dc.contributor.author | Wong, Ronald J. | |
dc.contributor.author | Flynn, Kaitlin J. | |
dc.contributor.author | Parraga-Leo, Antonio | |
dc.contributor.author | Wibrand, Camilla | |
dc.date.accessioned | 2024-07-16T09:41:29Z | |
dc.date.available | 2024-07-16T09:41:29Z | |
dc.date.issued | 2024-01-16 | |
dc.identifier.citation | 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. | es_ES |
dc.identifier.issn | 2666-3791 | |
dc.identifier.uri | http://hdl.handle.net/2183/38041 | |
dc.description | 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. | es_ES |
dc.description | 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. | es_ES |
dc.description.abstract | [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 The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals.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. | es_ES |
dc.description.sponsorship | We thank members of the Sirota Lab, University of California, San Francisco, for useful discussion. This study was supported by the March of Dimes (J.L.G., T.T.O., A.R., A.S.T., V.C., C.W.Y.H., R.J.W., K.J.F., G.A., I.K., J.B., A.N., J.G., Z.W., P.N., A.K., I.B., E.K., S.J., S.N., Y.S.L., P.R.B., D.A.M., S.V.L., J.A., D.K.S., N.Aghaeepour, J.C.C., M.S.) and R35GM138353 (N.Aghaeepour), 1R01HL139844 (N.Aghaeepour), 3P30AG066515 (N.Aghaeepour), 1R61NS114926 (N.Aghaeepour), 1R01AG058417 (N.Aghaeepour), R01HD105256 (N.Aghaeepour, M.S.), P01HD106414 (N.Aghaeepour), R01GM140464 (J.G., Z.W., G.C., Z.-Z.T.), NSF DMS-2054346 (J.G., Z.W., G.C., Z.-Z.T.); the Burroughs Welcome Fund (N.Aghaeepour); the Alfred E. Mann Foundation (N.Aghaeepour); and the Robertson Foundation (N.Aghaeepour). A.P.-L. and P.D.-G. are receiving honoraria from the IVI Foundation. | es_ES |
dc.description.sponsorship | United States. National Institute of General Medical Sciences; R35GM138353 | es_ES |
dc.description.sponsorship | United States. National Institutes of Health; 1R01HL139844 | es_ES |
dc.description.sponsorship | United States. National Institutes of Health; 3P30AG066515 | es_ES |
dc.description.sponsorship | United States. National Institutes of Health; 1R61NS114926 | es_ES |
dc.description.sponsorship | United States. National Institute on Aging; 1R01AG058417 | es_ES |
dc.description.sponsorship | United States. National Institute of Child Health and Human Development; R01HD105256 | es_ES |
dc.description.sponsorship | United States. National Institute of Child Health and Human Development; P01HD106414 | es_ES |
dc.description.sponsorship | United States. National Institutes of Health; R01GM140464 | es_ES |
dc.description.sponsorship | United States. National Science Foundation; DMS-2054346 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.xcrm.2023.101350 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Preterm birth | es_ES |
dc.subject | Vaginal microbiome | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Predictive modeling | es_ES |
dc.subject | Crowdsourced | es_ES |
dc.subject | Microbiome | es_ES |
dc.subject | 16S harmonization | es_ES |
dc.subject | DREAM challenge | es_ES |
dc.title | Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Cell Reports Medicine | es_ES |
UDC.volume | 5 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 101350 | es_ES |