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Showing 1–4 of 4 results
Advanced filters: Author: Abdulkadir Elmas Clear advanced filters

  • Self-supervised learning (SSL) is increasingly used to train pathology foundation models. Here, the authors introduce a pathology benchmark set generated during standard clinical workflows that includes multiple cancer and disease types; then leverage it to assess the performance of multiple public SSL pathology foundation models and to provide best practices for model training and selection.

    • Gabriele Campanella
    • Shengjia Chen
    • Chad Vanderbilt
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12

  • Dysregulated phosphorylation is well-known in cancers, but it has largely been studied in isolation from mutations. Here the authors introduce HotPho, a tool that can discover spatial interactions between phosphosites and mutations, which are associated with activating mutation and genetic dependencies in cancer.

    • Kuan-lin Huang
    • Adam D. Scott
    • Li Ding
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-13

  • Elmas et al. develop an algorithm, OPPTI, to identify overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data from over 1000 cases. They reveal that protein-level aberrations, which are sometimes not observed using genomics, represent cancer vulnerabilities that may be targeted in precision oncology.

    • Abdulkadir Elmas
    • Serena Tharakan
    • Kuan-lin Huang
    ResearchOpen Access
    Communications Biology
    Volume: 4, P: 1-13