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

Golob, J. L., Oskotsky, T. T., Tang, A. S., Roldan, A., Chung, V., Ha, C. W. Y., Wong, R. J., Flynn, K. J., Parraga-Leo, A., Wibrand, C., Minot, S. S., Oskotsky, B., Andreoletti, G., Kosti, I., Bletz, J., Nelson, A., Gao, J., Wei, Z., Chen, G., … Sirota, M. (2024). Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research. Cell Reports Medicine, 5(1), 101350. https://doi.org/10.1016/j.xcrm.2023.101350
Authors:
Jonathan L Golob
Tomiko T Oskotsky
Alice S Tang
Alennie Roldan
Verena Chung
Connie W Y Ha
Ronald J Wong
Kaitlin J Flynn
Antonio Parraga-Leo
Camilla Wibrand
Samuel S Minot
Boris Oskotsky
Gaia Andreoletti
Idit Kosti
Julie Bletz
Amber Nelson
Jifan Gao
Zhoujingpeng Wei
Guanhua Chen
Zheng-Zheng Tang
Pierfrancesco Novielli
Donato Romano
Ester Pantaleo
Nicola Amoroso
Alfonso Monaco
Mirco Vacca
Maria De Angelis
Roberto Bellotti
Sabina Tangaro
Abigail Kuntzleman
Isaac Bigcraft
Stephen Techtmann
Daehun Bae
Eunyoung Kim
Jongbum Jeon
Soobok Joe
Kevin R Theis
Sherrianne Ng
Yun S Lee
Patricia Diaz-Gimeno
Phillip R Bennett
David A MacIntyre
Gustavo Stolovitzky
Susan V Lynch
Jake Albrecht
Nardhy Gomez-Lopez
Roberto Romero
David K Stevenson
Nima Aghaeepour
Adi L Tarca
James C Costello
Marina Sirota
Affiliated Authors:
Gustavo Stolovitzky
Columbia Affiliation:
Subjects:
Premature Birth (MeSH)
Crowdsourcing (MeSH)
Microbiota (MeSH)
Author Keywords:
16s harmonization
dream challenge
crowdsourced
machine learning
microbiome
predictive modeling
preterm birth
vaginal microbiome
elink
Publication Type:
Article
Unique ID:
10.1016/j.xcrm.2023.101350
PMID:
38134931
DOI:
10.1016/j.xcrm.2023.101350
Journal:
Cell Reports Medicine
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record

Record Created: