Yuanfei Sun

The paper develops an AI framework to analyze blood scans and predict patient outcomes by characterizing tumor-immune ecosystems in circulation.

This paper introduces an antibody language model (AbLM) to accelerate IgG screening and design for broad-spectrum antiviral therapy, achieving higher precision in predicting variant-resistant antibodies.

The paper identifies a rare subset of CD4 and CD8 double positive T cells that form clusters with circulating tumor cells, promoting cancer metastasis through immune suppression and integrin-mediated interactions.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

The paper evaluates 23 imputation models for cross-cell type epigenomic data, finding quality assessment challenging due to distributional shifts, data availability, and redundant metrics.

The paper investigates whether multi-modal data and auxiliary tasks improve inter-protein contact prediction, finding that multi-modal inputs enhance precision but auxiliary tasks have limited benefits.

This paper introduces a weakly supervised regression model to predict pathogenicity of missense variants and infer molecular mechanisms from imprecise annotations.