ECKO: Explainable Clinical Knowledge for Oncology
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915569344708608 |
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| author | Silva, Marta Contreiras Faria, Daniel Balbi, Laura Nunes, Susana Rodrigues, Ana Filipa Palkowski, Aleksander Waleron, Michal Daghir-Wojtkowiak, Emilia Kallor, Ashwin Adrian Battail, Christophe Corazza, Federico Maria Fiorelli, Manuel Stellato, Armando Alfaro, Javier Antonio Zanzotto, Fabio Massimo Pesquita, Catia |
| author_facet | Silva, Marta Contreiras Faria, Daniel Balbi, Laura Nunes, Susana Rodrigues, Ana Filipa Palkowski, Aleksander Waleron, Michal Daghir-Wojtkowiak, Emilia Kallor, Ashwin Adrian Battail, Christophe Corazza, Federico Maria Fiorelli, Manuel Stellato, Armando Alfaro, Javier Antonio Zanzotto, Fabio Massimo Pesquita, Catia |
| contents | Personalized oncology aims to tailor treatment strategies to the unique molecular and clinical profiles of individual patients, moving beyond the traditional paradigm of treating the disease not the patient. Achieving this vision requires the integration and interpretation of vast, heterogeneous biomedical data within a meaningful scientific framework. Knowledge graphs, structured according to biomedical ontologies, offer a powerful approach to contextualize and interconnect diverse datasets, enabling more precise and informed clinical decision-making.
We present ECKO (Explainable Clinical Knowledge for Oncology), a comprehensive knowledge graph that integrates 33 biomedical ontologies and aggregates data from multiple studies to create a unified resource optimized for data-driven clinical applications in oncology. Designed to support personalized drug recommendations, ECKO facilitates the identification of optimal therapeutic options by linking patient-specific molecular data to relevant pharmacological knowledge. It provides transparent, interpretable explanations for drug recommendations, fostering greater trust and understanding among clinicians and researchers. This resource represents a significant advancement toward explainable, scalable, and clinically actionable personalized medicine in oncology, with potential applications in biomarker discovery, treatment optimization, and translational research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ECKO: Explainable Clinical Knowledge for Oncology Silva, Marta Contreiras Faria, Daniel Balbi, Laura Nunes, Susana Rodrigues, Ana Filipa Palkowski, Aleksander Waleron, Michal Daghir-Wojtkowiak, Emilia Kallor, Ashwin Adrian Battail, Christophe Corazza, Federico Maria Fiorelli, Manuel Stellato, Armando Alfaro, Javier Antonio Zanzotto, Fabio Massimo Pesquita, Catia Quantitative Methods Personalized oncology aims to tailor treatment strategies to the unique molecular and clinical profiles of individual patients, moving beyond the traditional paradigm of treating the disease not the patient. Achieving this vision requires the integration and interpretation of vast, heterogeneous biomedical data within a meaningful scientific framework. Knowledge graphs, structured according to biomedical ontologies, offer a powerful approach to contextualize and interconnect diverse datasets, enabling more precise and informed clinical decision-making. We present ECKO (Explainable Clinical Knowledge for Oncology), a comprehensive knowledge graph that integrates 33 biomedical ontologies and aggregates data from multiple studies to create a unified resource optimized for data-driven clinical applications in oncology. Designed to support personalized drug recommendations, ECKO facilitates the identification of optimal therapeutic options by linking patient-specific molecular data to relevant pharmacological knowledge. It provides transparent, interpretable explanations for drug recommendations, fostering greater trust and understanding among clinicians and researchers. This resource represents a significant advancement toward explainable, scalable, and clinically actionable personalized medicine in oncology, with potential applications in biomarker discovery, treatment optimization, and translational research. |
| title | ECKO: Explainable Clinical Knowledge for Oncology |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2510.18929 |