Researchers have developed HEIST, a hierarchical graph transformer model designed to analyze spatial transcriptomics and proteomics data. This model represents tissues as hierarchical graphs, capturing both spatial cell relationships and internal gene expression networks. Pretrained on a massive dataset of cells from various organs, HEIST demonstrates generalization to new data types and achieves state-of-the-art performance in tasks like clinical outcome prediction and cell type annotation. Another study introduces STAMP, a framework that uses spatial transcriptomics to guide pathology foundation models, enhancing their ability to infer molecular profiles from histology images by aligning transcriptomic data into functional pathways. AI
IMPACT These models advance AI's capability in biological research, enabling deeper understanding of cellular processes and disease prediction from complex omics data.
RANK_REASON The cluster contains two academic papers describing novel AI models and frameworks for biological data analysis.
- HumanST-1k
- pathology foundation models
- Spatial Transcriptomics-guided Alignment for Molecular Profiling
- STAMP
- Hiren Madhu
- proteomics
- spatial transcriptomics
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