Chia-Yi Chu
Academic and research departments
AI for health and wellbeing, Surrey Institute for People-Centred Artificial Intelligence (PAI).Â鶹ÊÓƵ
My research project
Genetics of pregnancy loss through implementation of machine learning approaches to omics dataThis project will explore big omics data and apply efficient analytical and artificial intelligence (AI) approaches for identifying novel biomarkers for woman’s reproductive health conditions. Women’s reproductive health is the least systematically evaluated set of phenotypes in human genetics, contrary to its importance at individual level. The prevalence of women’s reproductive issues rapidly increases with ageing of human populations. The increasing age at conception leads to fertility problems, including miscarriage, pregnancy loss and stillbirth. In-vitro fertilisation industry development and its popularisation exacerbate issues related to pregnancy losses (PL). Genetic studies demonstrated contribution of hereditable factors to susceptibility of PL but haven’t benefited from the recent technological development and availability of large datasets to the same extent as other common diseases. AI and machine learning approaches could be implemented for prediction of such outcomes. This project will provide insights into the genetics pregnancy loss and related conditions.
Supervisors
This project will explore big omics data and apply efficient analytical and artificial intelligence (AI) approaches for identifying novel biomarkers for woman’s reproductive health conditions. Women’s reproductive health is the least systematically evaluated set of phenotypes in human genetics, contrary to its importance at individual level. The prevalence of women’s reproductive issues rapidly increases with ageing of human populations. The increasing age at conception leads to fertility problems, including miscarriage, pregnancy loss and stillbirth. In-vitro fertilisation industry development and its popularisation exacerbate issues related to pregnancy losses (PL). Genetic studies demonstrated contribution of hereditable factors to susceptibility of PL but haven’t benefited from the recent technological development and availability of large datasets to the same extent as other common diseases. AI and machine learning approaches could be implemented for prediction of such outcomes. This project will provide insights into the genetics pregnancy loss and related conditions.