SOURCE-LINKED INTELLIGENCE

Recurrent miscarriage as a complex phenotype: Harnessing large-scale clinical data to uncover underlying biological pathways

CORDIS · observation · Publication date unknown

ctronic 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 cli

Read original source ↗ Open in workspace

recordType

award

status

CLOSED

region

EU

value

173080.8

unit

EUR