Zhongyu Wei

This paper introduces MPOGAN, a generative adversarial network for designing antimicrobial peptides with multiple desired properties, showing improved performance in antimicrobial activity and reduced cytotoxicity.

This paper introduces DeepPRS, a deep learning-based polygenic risk score model that outperforms existing methods in predicting risk for common diseases using genome-wide genotype data.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

This paper introduces ScHiC-Rep, a framework for single-cell Hi-C clustering that improves imputation and feature extraction, outperforming existing methods on human and mouse datasets.

The paper proposes DeepMNE-CNN, a method that integrates multiple biological networks using semi-supervised autoencoders and convolutional neural networks to improve gene function prediction.

The paper introduces a neural network framework, MDA-CNN, for predicting miRNA-disease and miRNA-phenotype associations, showing improved performance over existing methods.