Shao Li

Transfer Learning and Permutation-Invariance improving Predicting Genome-wide, Cell-Specific and Directional Interventions Effects of Complex Systems

This paper develops a deep learning model called SETComp to predict intervention effects of complex systems using transfer learning and permutation-invariance, achieving significant accuracy improvements over baselines.

Network-based molecular subtyping of acral melanoma

The paper identifies two subtypes of acral melanoma with distinct clinical and molecular features using multi-omics data and network-based analysis.

Development of attention-based robust deep learning model for tongue diagnosis by smartphone

The paper develops a deep learning model for smartphone-based tongue diagnosis, showing improved accuracy and robustness to environmental changes compared to baseline models.

Effect of oncolytic virotherapy on immune microenvironment in immune subtypes identified in gastric cancer and hepatocellular carcinoma

The paper assesses immune subtypes in gastric and hepatocellular carcinoma using TCGA data and evaluates the effect of synthetic oncolytic virotherapy on immune microenvironment.

CAMP: a Convolutional Attention-based Neural Network for Multifaceted Peptide-protein Interaction Prediction

CAMP predicts peptide-protein interactions and binding residues using a convolutional attention-based neural network, outperforming existing methods on benchmark datasets.

A comprehensive analysis of single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer

This study constructs a single-cell transcriptome network of gastric premalignant lesions and early gastric cancer, identifying novel markers and signatures with clinical implications.