A Multimodal Foundation Model of Spatial Transcriptomics and Histology for Biological Discovery and Clinical Prediction

1. 1Train a foundation model on large datasets of spatially resolved transcriptomic profiles with matched histology images
2. 2Use the model to generate rich representations of biological tissues that capture both molecular and morphological information
3. 3Apply the model to various biological discovery and clinical prediction tasks, such as identifying disease mechanisms and predicting patient outcomes
4. 4Evaluate the model's performance on benchmark datasets and compare it to existing methods

This research benefits bioinformaticians, computational biologists, and clinicians who work with spatial transcriptomics and histology data, as it provides a powerful tool for integrating and analyzing these data types

💡 The integration of spatial transcriptomics and histology data using a foundation model can provide a more comprehensive understanding of biological tissues and improve clinical prediction

🔬 Introducing STORM, a multimodal foundation model that combines spatial transcriptomics & histology for biological discovery & clinical prediction! 🚀