{"ID":2878933,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17389","arxiv_id":"2508.17389","title":"Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.17389","url_pdf":"https://arxiv.org/pdf/2508.17389v1","authors":"[\"Bokai Zhao\",\"Weiyang Shi\",\"Hanqing Chao\",\"Zijiang Yang\",\"Yiyang Zhang\",\"Ming Song\",\"Tianzi Jiang\"]","published":"2025-08-24T14:53:12Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":610527,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2878933,"paper_url":"https://arxiv.org/abs/2508.17389","paper_title":"Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction","repo_url":"https://github.com/Bokai-Zhao/NPF","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}