11 citations · 17 across the 5 of their papers we have counts for
5 papers
Ensembling improves stability and power of feature selection for deep learning models
Prashnna K Gyawali, Xiaoxia Liu, James Zou +1
With the growing adoption of deep learning models in different real-world domains, including computational biology, it is often necessary to understand which data features are esse…
Improving genetic risk prediction across diverse population by disentangling ancestry representations
Prashnna K Gyawali, Yann Le Guen, Xiaoxia Liu +3
Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with sim…
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants
Peyman H. Kassani, Fred Lu, Yann Le Guen +1
Deep neural networks (DNN) have been used successfully in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging…
A Weighted U Statistic for Genetic Association Analyses of Sequencing Data
Changshuai Wei, Ming Li, Zihuai He +3
With advancements in next generation sequencing technology, a massive amount of sequencing data are generated, offering a great opportunity to comprehensively investigate the role…
Modeling and Testing for Joint Association Using a Genetic Random Field Model
Zihuai He, Min Zhang, Xiaowei Zhan +1
Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains large…