Liang Chen

xNNPCD identifies regulators of programmed cell death by integrating CRISPR perturbation data with gene dependency profiles to predict therapeutic agents for apoptosis and ferroptosis.

This paper benchmarks 20 computational methods for multi-omics biomarker discovery in cancer using real and simulated datasets, finding that DeePathNet and Deep-KEGG perform best.

This study identifies conserved alternative splicing events associated with maximum lifespan across mammals, showing distinct patterns in the brain compared to peripheral tissues.

Shiba and scShiba systematically identify differential RNA splicing across RNA-seq platforms with high accuracy and low false positives, even with small sample sizes.

The paper develops an interpretable deep learning model to predict cancer patient survival using drug prescriptions and personal transcriptomes, achieving high accuracy and identifying pathways that influence survival.

The paper identifies genetic variants influencing NMD regulation across human tissues and finds they are tissue-specific, enriched in gene regions and regulatory elements, and colocalize with disease SNPs.

This study shows that arginine methylation of SARS-CoV-2 N protein by PRMT1 is essential for RNA binding and suppression of stress granules, and inhibiting this methylation reduces viral replication.

The paper identifies transcriptional trajectories in human kidney disease progression using machine learning and validates findings in a mouse model.