Hua Chai

This paper introduces UISNet, a deep learning framework for breast cancer outcomes prediction that improves accuracy and interpretability by integrating uncertainty-based gradients and biological knowledge.

scAdapt improves single-cell RNA-seq classification across platforms and species by aligning domain distributions using adversarial learning.

The paper develops a deep learning framework to integrate multi-omics data for cancer prognosis, showing improved risk prediction across 15 cancers and identifying prognostic markers in breast cancer.

The paper introduces TDimpute, a transfer learning-based deep neural network for imputing missing RNA-seq data using DNA methylation, showing improved accuracy across 16 cancer datasets.