Journal
BIOINFORMATICS
Volume 35, Issue 1, Pages 95-103Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty537
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Funding
- March of Dimes Prematurity Research Center at Stanford
- Bill and Melinda Gates Foundation [OPP1112382]
- Department of Anesthesiology, Perioperative and Pain Medicine and Children Health Research Institute at Stanford University
- Ann Schreiber Mentored Investigator Award from the Ovarian Cancer Research Fund [OCRF 292495]
- Canadian Institute of Health Research (CIHR) [CIHR 321510]
- International Society for Advancement of Cytometry Scholarship
- Fonds de Recherche du Quebec-Nature et Technologies (FRQNT) [211363]
- NIH [5U54DK10255603]
- Burrows Wellcome Fund
- NOMIS Foundation
- Thomas C. and Joan M. Merigan Endowment at Stanford University
- [K01LM012381]
- Bill and Melinda Gates Foundation [OPP1112382] Funding Source: Bill and Melinda Gates Foundation
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Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.
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