题目：GWAS-based Machine Learning Approaches for Predicting AMD Progression
报告人： Ying Ding (speaker) and Wei Chen
摘要: Recent advances in machine learning have made extraordinary achievements in establishing flexible and powerful prediction models. The genomewide association studies (GWAS) of Age-related Macular Degeneration (AMD), a progressive eye disease, is the first and most successful GWAS research, where the massive GWAS data provide unprecedented opportunities to study disease risk and progression. Motivated by the need to establish a flexible and reliable prediction model for AMD progression, we develop a novel framework, which builds deep neural networks on time-to-event outcomes to effectively extract features from the wealthy GWAS data. Using data from two large randomized clinical trial on AMD progression, Age Related Eye Disease Study (AREDS) and AREDS2, we apply our method to develop and evaluate three machine-learning-based prediction models to predict the risk of progression to late-AMD given the patient’s clinical and genetic profiles. The result provides valuable insights into early prevention and tailored intervention.
时间: 10:30 am – 11:30 am, July 6 (Saturday), 2019
地点: Room 108, West Building, Gulou Campus Department of Mathematics, Nanjing University
Ying Ding, Department of Biostatistics, School of Public Health, University of Pittsburgh, PA
Wei Chen, Department of Pediatrics, School of Medicine, University of Pittsburgh, UPMC Children’s Hospital of Pittsburgh, PA