ArticleBioinformatics (Oxford, England)2024
Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Review
- Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency.Bioengineering (Basel, Switzerland) · 2025Review
- A variational graph-partitioning approach to modeling protein liquid-liquid phase separation.Cell reports. Physical science · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
motivationSpatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.
resultsHere, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest. AVAILABILITY AND IMPLEMENTATION: https://github.com/song0309/asGNN/.
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