Elijah Cole

GenBio-PathFM is a foundation model for histopathology that achieves state-of-the-art performance with less training data by using a diverse data curation pipeline and a dual-stage learning strategy.

This study evaluates foundation models for predicting cellular responses to perturbations, finding that some pretrained embeddings improve predictions while others do not.

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

AIDO.ModelGenerator enables rapid, reproducible multimodal foundation model development by integrating datasets, pre-trained models, and experiment recipes to improve biological predictions.

This paper benchmarks foundation model representations for predicting cellular perturbation responses, identifying effective representation learning strategies.