Development of new computational tools to enhance the risk predictions for age-related diseases
Approved Research ID: 65441
Approval date: November 25th 2020
The prevalence of chronic disease significantly increases depending on genetic variations with aging. More than 50% of the diseases of people over 60 can be prevented through lifestyle changes. Thus, study of identifying which genetic variations and which factors within lifestyle are associated with each disease is essential in our project.
We aim to develop innovative computational tools to predict risks across the entire range of human age-related disease. We will involve combinations of some advanced machine learning algorithms that will use the huge amounts of genotype, phenotype, and environmental data of UK Biobank. The algorithms will extract some important features to understand underlying genetic factors, phenotype factors, and environmental factors behind age-related disease. The duration of this project will be three years, from 2020 to 2023.
The result of this project will improve human health and healthcare by enhancing the predictive accuracy of our new computational model towards age-related disease. Our tools will give us noble candidates for early diagnosis for age-related disease. Further, our academic findings in this project can provide valid evidence to new drug targets in genetic level.