Leah V. Schaffer

The paper introduces MIRAGE, a multi-modal generative model that integrates protein sequence, interaction, and localization data to improve function prediction and module detection.

The paper evaluates 46 human interactomes to identify which are best for disease gene discovery and interaction prediction, finding large networks like HumanNet perform better for disease genes while smaller ones like DIP excel in prediction.

ProteinAligner improves protein function and property prediction by integrating sequence, structure, and literature text modalities in a multi-modal pretraining framework.

The paper introduces the CM4AI project, which aims to create AI-ready datasets of human cell architecture using multimodal data from cell lines.

The paper integrates protein imaging and interaction data to create a unified map of eukaryotic cell structure, revealing new subcellular systems and protein associations.