Sonia Maria Krissmer

This paper introduces coding agents that generate interpretable single-cell embeddings from literature-based gene programs without training data or databases.

The paper introduces mmContext, a framework for multimodal contrastive learning of omics and text data, and demonstrates its utility in RNA-Seq and text integration tasks.

The paper fine-tunes biomedical language models to integrate text-based knowledge with scRNA-seq data, enhancing cell identity preservation and adding functional and disease-related context.