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Recent advances in both genomics, transcriptomics, and proteomics have allowed for the development of novel technologies with greater capabilities for biomarker profiling and discovery. When coupled with high-content data analysis, these multiomic approaches have allowed researchers to generate high-efficiency data to enable new correlations and insights between the genome, transcriptome, and proteome at a molecular level in health and disease. For example, an area of unmet need where multiomics is poised to impact significantly is pregnancy-related complications such as preterm birth and preeclampsia. A better understanding of the molecular mechanisms of feto-maternal changes during pregnancy could help save lives and improve birthing processes.

In this GEN webinar, our distinguished presenters—members of the Gaudillière lab at Stanford University—will describe how they employed a translational approach that combines single-cell mass cytometry with high-plex proteomic and metabolomic analyses to study the role of the human immune system in the pathobiology of fundamental clinical problems, including pregnancy pathologies. We will also learn how our presenters use novel machine-learning methods to train multiomic models and identify biologically relevant predictive biomarkers to develop robust diagnostics rooted in a precise understanding of underlying pathobiological mechanisms. 


A live Q&A session followed the presentation, offering a chance to pose questions to our expert panelists.

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Ina Steltzer
Ina Stelzer, PhD
Stanford University
Julien Hedou
Julien Hedou
Data Analyst
Stanford University
Dorien Feyaerts,
Dorien Feyaerts, PhD
Postdoctoral Fellow
Stanford University