SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911668549713920 |
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| author | Nasser, Sahar Almahfouz Borja, Juan Francisco Pesantez Liu, Jincheng Manandhar, Sandeep Shiromani, Shikhar Hasan, Mohammad Tanvir Wang, Zenghan Ghosh, Suman Li, Jinchu Xu, Xuejian Iyer, Aniket Ramkrishnan Tokuyama, Naoto Shah, Twisha Pathak, Tilak Kumaresan, Soundharya Abe, Yohei Maurya, Himanshu Madabhushi, Anant |
| author_facet | Nasser, Sahar Almahfouz Borja, Juan Francisco Pesantez Liu, Jincheng Manandhar, Sandeep Shiromani, Shikhar Hasan, Mohammad Tanvir Wang, Zenghan Ghosh, Suman Li, Jinchu Xu, Xuejian Iyer, Aniket Ramkrishnan Tokuyama, Naoto Shah, Twisha Pathak, Tilak Kumaresan, Soundharya Abe, Yohei Maurya, Himanshu Madabhushi, Anant |
| contents | Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remains largely intuition-driven, guided by fragmented literature rather than rigorous biological validation. We introduce SAGE (Structured Agentic system for hypothesis Generation and Evaluation), a multi-agent framework that grounds biomarker discovery in biological evidence through three mechanisms: (i) knowledge-graph-anchored hypothesis generation via multi-path ontological reasoning, (ii) a debate-based multi-agent novelty assessment that stress-tests candidate biomarkers against existing literature, and (iii) an end-to-end automated validation pipeline that translates hypotheses directly into executable analyses on multimodal pathology datasets. Together, these components shift biomarker discovery from an intuition-driven, literature-browsing exercise into a structured, traceable reasoning process that clinicians and researchers can inspect, trust, and build upon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00953 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery Nasser, Sahar Almahfouz Borja, Juan Francisco Pesantez Liu, Jincheng Manandhar, Sandeep Shiromani, Shikhar Hasan, Mohammad Tanvir Wang, Zenghan Ghosh, Suman Li, Jinchu Xu, Xuejian Iyer, Aniket Ramkrishnan Tokuyama, Naoto Shah, Twisha Pathak, Tilak Kumaresan, Soundharya Abe, Yohei Maurya, Himanshu Madabhushi, Anant Machine Learning Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remains largely intuition-driven, guided by fragmented literature rather than rigorous biological validation. We introduce SAGE (Structured Agentic system for hypothesis Generation and Evaluation), a multi-agent framework that grounds biomarker discovery in biological evidence through three mechanisms: (i) knowledge-graph-anchored hypothesis generation via multi-path ontological reasoning, (ii) a debate-based multi-agent novelty assessment that stress-tests candidate biomarkers against existing literature, and (iii) an end-to-end automated validation pipeline that translates hypotheses directly into executable analyses on multimodal pathology datasets. Together, these components shift biomarker discovery from an intuition-driven, literature-browsing exercise into a structured, traceable reasoning process that clinicians and researchers can inspect, trust, and build upon. |
| title | SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.00953 |