Ruheng Wang

SpatialOmicsLab provides an integrated environment with AI tools for spatial transcriptomics analysis, outperforming expert workflows in multiple tasks.

This study shows that the time of day affects cancer immunotherapy efficacy through a circadian clock-dependent chemokine axis in mice.

This study evaluates machine learning algorithms and feature selection stability in multi-omics microbiome datasets, finding that nonlinear models and multi-omics integration improve predictive performance and reproducibility.

DiffusionST improves spatial transcriptomics data quality and identifies spatial domains through a diffusion model and graph convolutional network.

This paper introduces MIDI, a model that predicts cancer drug response and explains drug-target interactions using an attention mechanism, outperforming existing methods in prediction and interpretability.

DenoiseST is a deep learning method for spatial transcriptomics that improves data imputation, clustering, and identification of functionally variable genes by reducing dropout noise.

This paper introduces MucLiPred, a deep learning model using dual contrastive learning to improve prediction of protein-ligand interactions and identify binding residues, showing superior performance over existing methods.

DeepBIO is a deep-learning platform that automates biological sequence analysis, offering 42 algorithms and visualization tools for functional prediction and annotation.

This paper introduces PHAT, a deep graph learning framework for peptide secondary structure prediction, achieving better accuracy and interpretability for short peptides.