Ling‐Yun Wu

The paper introduces FSDiffNet, a neural network for differential graph inference, which outperforms existing methods on simulated and real datasets related to autism and breast cancer.

The paper constructs a transcriptomic atlas of immune cells across human lifespan and develops a biological age prediction model using scRNA-seq and bulk RNA-seq data.

PathExpSurv integrates known biological pathways with neural networks to improve survival analysis in cancer, identifying disease-related genes through pathway expansion.

PENCIL identifies phenotype-relevant cell subpopulations from single-cell data using a supervised learning framework with feature selection and trajectory prediction capabilities.

SCARP improves scATAC-seq analysis by incorporating peak location information to better characterize co-accessibility and cell heterogeneity.

This paper introduces a single-cell computational method to predict cancer immunotherapy response by analyzing CD8+ T cell exhaustion profiles.