Ban Wang

The paper introduces macroscopic metrics to analyze RNA-seq data and reveal chromatin modifications in aging and disease, showing their sensitivity to chromatin structure changes and effectiveness in distinguishing cell states.

The paper uses deep learning to design optimized 5'UTRs for mRNA-delivered gene editing, showing improved editing efficiency across cell types and targets.

The paper identifies transcriptome diversity as a major source of systematic bias in RNA-seq data and shows it explains significant variability in gene expression across organisms and datasets.

The paper develops a model to predict the effect of 5' UTR variants on translation using experimental data and deep learning, showing its application to disease-associated SNVs.