STProtein: predicting spatial protein expression from multi-omics data
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Abstract (arXiv)
The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi-task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi-omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi-omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological "Dark Matter".
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 38 references that are in the map. First posted on arXiv 5 Feb 2026.
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References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2021How Attentive are Graph Attention Networks?cited by 835
- 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
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