Qinghua Jiang

RNASTOP predicts and optimizes mRNA chemical stability using deep learning, improving accuracy by 13% and reducing free energy in a vaccine sequence.

This paper introduces stMSA, a deep learning model for integrating spatial omics data, improving domain detection, matching, and alignment across tissue slices.

The paper creates a comprehensive human cortical atlas using single-cell and spatial transcriptomics data to reveal cellular organization and functional correlations in the cerebral cortex.

DeepCCI uses deep learning to identify cell-cell interactions from scRNA-seq data, showing accurate and effective results across diverse datasets.

The paper uses molecular dynamics simulation to analyze how the N439K mutation in SARS-CoV-2's spike protein affects its binding to ACE2 and resistance to neutralizing antibodies.

This study uses single-cell RNA sequencing to analyze immune responses in COVID-19 patients, identifying key immune cell changes and molecular pathways linked to disease severity.

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

This paper uses Mendelian randomization to show that genetically increased serum calcium levels are associated with reduced Alzheimer’s disease risk.