Zhaoyu Li

This paper introduces a training-free FFT-based method for efficient and interpretable epigenomic data compression, achieving high fidelity and speed compared to autoencoders.

The paper introduces a deep learning method using contrastive learning to predict gene expression from histone modifications without arbitrary thresholds, showing consistent performance across datasets.

This study uses machine learning to identify novel shared targets in glioblastoma and osteosarcoma, revealing C2ORF72 as a potential therapeutic and diagnostic target.

This study uses classical statistical methods to identify survival-related genes in breast cancer, finding two novel targets and validating results through bootstrapping.