Application of computational biology approaches to identify and develop biomarkers to predict pregnancy disorders and guide clinical practice.

Research Opportunity
PhD students, Masters by Research
Department / Centre
Obstetrics and Gynaecology
Location
Royal Women’s Hospital
Primary Supervisor Email Number Webpage
Professor Eva Dimitriadis eva.dimitriadis@unimelb.edu.au +61 3 83452215
Co-supervisor Email Number Webpage
A/Professor Kim-Anh Le Cao kimanh.lecao@unimelb.edu.au
Dr Ellen Menkhorst ellen.menkhorst@unimelb.edu.au

Summary Pregnancy complications such as preeclampsia, fetal growth restriction and pre-term birth affect up to 10% of all pregnancies. This project will apply bioinformatics tools to develop biomarkers which may identify which women will develop preeclampsia leading to a predictive test for preeclampsia and personalised treatment options.

Project Details

Pregnancy complications such as preeclampsia, fetal growth restriction and pre-term birth affect up to 10% of all pregnancies. Preeclampsia is a severe multi system disorder whereby the mother develops hypertension and proteinuria before 20 weeks of pregnancy. There is strong evidence that these complications are caused by abnormal placental development the during the 1st trimester, long before symptoms develop. By this time, the placenta is damaged and releases factors into the mother’s blood leading to widespread endothelial cell damage and the symptoms of preeclampsia.  Women can develop early onset, late onset and term preeclampsia, all of which can have severe consequences for both mother and baby not just during pregnancy but also later in life. Alarmingly there are no non-invasive tests that can predict all women who will develop preeclampsia and few treatment options. This project will apply various bioinformatics and computational tools to develop biomarkers which may identify which women will develop preeclampsia. This may lead to developing a predictive test for preeclampsia and lead to personalised treatment options.

R programming skills are required to make most of this project, some background statistical knowledge and a keen interest in computational biology.



Faculty Research Themes

School Research Themes

Child Health in Medicine, Cardiometabolic , Women's Health



Research Opportunities

PhD students, Masters by Research
Students who are interested in joining this project will need to consider their elegibility as well as other requirements before contacting the supervisor of this research

Graduate Research application

Honours application

Key Contact

For further information about this research, please contact a supervisor.

Department / Centre

Obstetrics and Gynaecology

Research Node

Royal Women’s Hospital

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