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Main Authors: 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
Format: Recurso digital
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Published: Zenodo 2025
Online Access:https://doi.org/10.5281/zenodo.17363402
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Table of 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>

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