Difei Tang

This paper constructs a knowledge graph of CAR T cell signaling interactions using automated text mining and large language models to aid in CAR design.

NL2BLTL automates translating natural language biological hypotheses into bounded linear temporal logic specifications, achieving high accuracy and syntactic validity.

The paper introduces CELESTA, a framework for biomedical context classification that improves accuracy by integrating semi-supervised multi-task learning and a large-scale annotated dataset.

This work improves a GBM mechanistic model and evaluates NLP and LLM approaches for model refinement using graph metrics and literature curation.

The paper introduces the BioRECIPE format for representing biological models to enable human-machine interaction and model development.

VIOLIN reconciles heterogeneous interaction graphs with extracted molecular interactions, showing stable and interpretable results across different models and datasets.