Si Liu

Concordia detects spatial domains in population-level spatial proteomics data using a GNN-based framework, revealing a cancer-associated fibroblast subset linked to clinical outcomes in lung cancer.

The paper develops computational tools to identify cancer-reactive T cells and neoantigen-specific TCRs, showing their variability across cancer types and predictive value for immunotherapy response.

The paper evaluates and improves a deep learning model for predicting TCR-HLA associations by addressing confounding factors and combining datasets.

This study improves TCR-based infection prediction by incorporating HLA allele information, showing enhanced accuracy for certain HLA alleles and insights into their roles in infection.

The paper introduces a neural network model, DePTH, to predict TCR-HLA associations from amino acid sequences and shows its association with cancer patient survival outcomes.