Gang Li

MOBER integrates transcriptomic data from cancer cell lines, xenografts, and clinical tumors using deep learning to remove batch effects and improve clinical translatability.

The paper trains a deep learning model to predict enzyme thermal adaptation using a dataset of enzymes labeled with optimal growth temperatures.

A substrate graph neural network plus protein-sequence CNN predicts enzyme kcat (test Pearson r 0.71), tracks mutation effects, and parameterizes enzyme-constrained models for 343 yeast/fungi species.

The paper develops scAR, a machine learning model to remove ambient noise from single-cell omics data across multiple technologies, showing high denoising accuracy.

This paper uses Bayesian modeling to identify temperature-dependent enzymes in yeast metabolism, revealing squalene epoxidase as a key rate-limiting enzyme and demonstrating its role in thermotolerance.

The paper develops machine learning models to predict microorganism growth temperatures and enzyme catalytic optima using proteome-wide amino acid composition data.