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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17363402 |
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| _version_ | 1866902086481870848 |
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| author | Castillo, Simon P Gautam, Tanishq Pinao, Karina Salvatierra, Maria Ester Serrano, Alejandra Ercan, Caner Rodriguez, Berta Leticia Acosta, Paul Chen, Pingjun Shokrollahi, Yasin Lau, Alexandria Kwong, Lawrence Huse, Jason Pan, Xiaoxi Mosaic, Patient Solis-Soto, Luisa Yuan, Yinyin |
| author_facet | Castillo, Simon P Gautam, Tanishq Pinao, Karina Salvatierra, Maria Ester Serrano, Alejandra Ercan, Caner Rodriguez, Berta Leticia Acosta, Paul Chen, Pingjun Shokrollahi, Yasin Lau, Alexandria Kwong, Lawrence Huse, Jason Pan, Xiaoxi Mosaic, Patient Solis-Soto, Luisa Yuan, Yinyin |
| contents | <p>Code and dataset for the review process of the manuscript "Hybrid generalist-specialist AI for scaling spatial pathomics".</p> <p><strong>Data provided in this repository is solely for journal peer review and must not be used for any other purpose.</strong></p> <p>Files:</p> <ul> <li><strong>metadata_tumorclass.csv</strong>: file with tumor type per case.</li> <li><strong>raw_dspproteomics.csv:</strong> file with raw counts DSP proteomics per ROI segment per case.</li> <li><strong>code_with_data.zip:</strong> zip file with a minimal dataset to test the Python code. It includes two subfolders for ROI prediction in spatial proteomics (DSP) and spatial transcriptomics (ST). In each case, comprehensive README files facilitate navigation.</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17363402 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid generalist-specialist AI for scaling spatial pathomics Castillo, Simon P Gautam, Tanishq Pinao, Karina Salvatierra, Maria Ester Serrano, Alejandra Ercan, Caner Rodriguez, Berta Leticia Acosta, Paul Chen, Pingjun Shokrollahi, Yasin Lau, Alexandria Kwong, Lawrence Huse, Jason Pan, Xiaoxi Mosaic, Patient Solis-Soto, Luisa Yuan, Yinyin <p>Code and dataset for the review process of the manuscript "Hybrid generalist-specialist AI for scaling spatial pathomics".</p> <p><strong>Data provided in this repository is solely for journal peer review and must not be used for any other purpose.</strong></p> <p>Files:</p> <ul> <li><strong>metadata_tumorclass.csv</strong>: file with tumor type per case.</li> <li><strong>raw_dspproteomics.csv:</strong> file with raw counts DSP proteomics per ROI segment per case.</li> <li><strong>code_with_data.zip:</strong> zip file with a minimal dataset to test the Python code. It includes two subfolders for ROI prediction in spatial proteomics (DSP) and spatial transcriptomics (ST). In each case, comprehensive README files facilitate navigation.</li> </ul> |
| title | Hybrid generalist-specialist AI for scaling spatial pathomics |
| url | https://doi.org/10.5281/zenodo.17363402 |