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RMCmplxPheno

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Recurrent miscarriage as a complex phenotype: Harnessing large-scale clinical da...

Recurrent miscarriage as a complex phenotype: Harnessing large-scale clinical data to uncover underlying biological pathways Recurrent miscarriage (RM) affects 1% of couples trying to conceive and has a wide range of negative physical and mental impacts yet still has few evidence-based, preventative treatments. RM is used as a catch-all term for an even... ver más

31/12/2023

UCPH

173K€

Presupuesto del proyecto: 173K€

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-12-31

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

I+D

Cerrada hace 3 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UCPH

173.08K€ | Lider

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Duración del proyecto: 18 meses Fecha Inicio: 2022-06-20
Fecha Fin: 2023-12-31

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 173K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Recurrent miscarriage (RM) affects 1% of couples trying to conceive and has a wide range of negative physical and mental impacts yet still has few evidence-based, preventative treatments. RM is used as a catch-all term for an event with diverse underlying causes. Broad categorisation hampers research targeting specific causes that could identify potential therapeutic avenues. This generalisation compounds trauma for patients and the path to successful pregnancy is unclear. As a MSCA fellow at the University of Copenhagen, I will generate deeper understanding of RM aetiology by identifying granular subgroups of RM and elucidating their underlying biology. To do so I will: 1) Establish novel phenotypes derived from clinical data in electronic health records, including ultrasound images, and use these phenotypes to identify clinical phenotypes driving current miscarriage classification systems. 2) Apply hypothesis-free unsupervised machine learning to clinical data to disentangle complex phenotypes of RM into clinically relevant subgroups. 3) Employ genetic analyses to characterise biological pathways underlying these RM subgroups and identify potential therapeutic avenues. This fellowship will allow me to apply my skills and expertise in large-scale biomedical data analysis and genetics to a new field in which I will pursue a long-term career. In particular it will provide training in field specific scientific knowledge (obstetrics and gynaecology), cutting edge techniques (machine learning) and transferable skills towards scientific leadership (research management). Taken together the outcomes of this interdisciplinary research will have ramifications for researchers, clinicians and patients. For researchers, a granular understanding of RM and its causes will enable discovery of novel therapeutic avenues. For clinicians, it would assist clinical decision making towards personalised treatments. For patients, alleviation of trauma through empowerment with information.

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