junxiang ding

This paper introduces GeneEmbedLLM, a framework using large language models to generate gene embeddings from multi-source textual annotations, achieving competitive performance in various downstream tasks.

This study introduces a method to predict clinical phenotypes from single-cell data using attention-based multiple instance learning, demonstrating its effectiveness in predicting disease severity and age-related phenotypes.