AI Research Catalyst Fund Awardees Virtual Seminar Series

“A Kernel Neural Network for High-dimensional Genomic Risk Prediction“

by Dr. Qing Lu
Professor in the Department of Biostatistics

Wednesday, December 8, 2021

ABSTRACT:

Artificial intelligence (AI) is a thriving research field with many successful applications in areas such as computer vision and speech recognition. Neural-network-based methods (e.g., deep learning) play a central role in modern AI technology. While neural-network-based methods also hold great promise for genetic research, the high-dimensionality of genetic data, the massive amounts of study samples, and complex relationships between genetic variants and disease outcomes bring tremendous analytic and computational challenges. To address these challenges, we propose a kernel-based neural network (KNN) method. KNN inherits features from both linear mixed models (LMM) and classical neural networks, and is designed for high-dimensional genetic data analysis. Unlike the classic neural network, KNN summarizes a large number of genetic variants into kernel matrices and uses the kernel matrices as input matrices. Based on the kernel matrices, KNN builds a feedforward neural network to model the complex relationship between genetic variants and disease outcomes. Minimum norm quadratic unbiased estimation and batch training are implemented in KNN to accelerate the computation, making KNN applicable to massive datasets with millions of samples. Through simulations, we demonstrate the advantages of KNN over LMM in terms of prediction accuracy and computational efficiency. We also apply KNN to the large-scale UK Biobank dataset, evaluating the role of a large number of genetic variants on multiple complex diseases.

Bio: 

Qing Lu’s research interests are primarily in statistical genetics and statistical learning. One area of research is to develop statistical learning methods (e.g., tree and deep learning) for high-dimensional genetic data analysis. In parallel with statistical learning research, he is also interested in developing and applying new statistical methods (e.g., U-statistic) for genetic association analysis and risk prediction analysis.

Wednesday, December 8, 2021
12:00PM – 1:00PM
Via Zoom

RSVP below. Instructions to attend talk via Zoom will be emailed to you.