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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.17389 |
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| _version_ | 1866918129840422912 |
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| author | Zhao, Bokai Shi, Weiyang Chao, Hanqing Yang, Zijiang Zhang, Yiyang Song, Ming Jiang, Tianzi |
| author_facet | Zhao, Bokai Shi, Weiyang Chao, Hanqing Yang, Zijiang Zhang, Yiyang Song, Ming Jiang, Tianzi |
| contents | Spatial proteomics maps protein distributions in tissues, providing transformative insights for life sciences. However, current sequencing-based technologies suffer from low spatial resolution, and substantial inter-tissue variability in protein expression further compromises the performance of existing molecular data prediction methods. In this work, we introduce the novel task of spatial super-resolution for sequencing-based spatial proteomics (seq-SP) and, to the best of our knowledge, propose the first deep learning model for this task--Neural Proteomics Fields (NPF). NPF formulates seq-SP as a protein reconstruction problem in continuous space by training a dedicated network for each tissue. The model comprises a Spatial Modeling Module, which learns tissue-specific protein spatial distributions, and a Morphology Modeling Module, which extracts tissue-specific morphological features. Furthermore, to facilitate rigorous evaluation, we establish an open-source benchmark dataset, Pseudo-Visium SP, for this task. Experimental results demonstrate that NPF achieves state-of-the-art performance with fewer learnable parameters, underscoring its potential for advancing spatial proteomics research. Our code and dataset are publicly available at https://github.com/Bokai-Zhao/NPF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction Zhao, Bokai Shi, Weiyang Chao, Hanqing Yang, Zijiang Zhang, Yiyang Song, Ming Jiang, Tianzi Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Spatial proteomics maps protein distributions in tissues, providing transformative insights for life sciences. However, current sequencing-based technologies suffer from low spatial resolution, and substantial inter-tissue variability in protein expression further compromises the performance of existing molecular data prediction methods. In this work, we introduce the novel task of spatial super-resolution for sequencing-based spatial proteomics (seq-SP) and, to the best of our knowledge, propose the first deep learning model for this task--Neural Proteomics Fields (NPF). NPF formulates seq-SP as a protein reconstruction problem in continuous space by training a dedicated network for each tissue. The model comprises a Spatial Modeling Module, which learns tissue-specific protein spatial distributions, and a Morphology Modeling Module, which extracts tissue-specific morphological features. Furthermore, to facilitate rigorous evaluation, we establish an open-source benchmark dataset, Pseudo-Visium SP, for this task. Experimental results demonstrate that NPF achieves state-of-the-art performance with fewer learnable parameters, underscoring its potential for advancing spatial proteomics research. Our code and dataset are publicly available at https://github.com/Bokai-Zhao/NPF. |
| title | Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction |
| topic | Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.17389 |