Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics
We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wide DNA methylation profiles that is widely employed in the clinic. Neuro-oncology research would benefit greatly from understanding the underlying art...
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| Hauptverfasser: | , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
20 February 2025
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| In: |
Nature Communications
Year: 2025, Jahrgang: 16, Pages: 1-11 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-025-57078-0 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-025-57078-0 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-025-57078-0 
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| Verfasserangaben: | Salvatore Benfatto, Martin Sill, David T.W. Jones, Stefan M. Pfister, Felix Sahm, Andreas von Deimling, David Capper & Volker Hovestadt |
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| 245 | 1 | 0 | |a Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics |c Salvatore Benfatto, Martin Sill, David T.W. Jones, Stefan M. Pfister, Felix Sahm, Andreas von Deimling, David Capper & Volker Hovestadt |
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| 520 | |a We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wide DNA methylation profiles that is widely employed in the clinic. Neuro-oncology research would benefit greatly from understanding the underlying artificial intelligence decision process, which currently remains unclear. Here, we describe an interpretable framework to explain the classifier’s decisions. We show that functional genomic regions of various sizes are predominantly employed to distinguish between different tumor classes, ranging from enhancers and CpG islands to large-scale heterochromatic domains. We detect a high degree of genomic redundancy, with many genes distinguishing individual tumor classes, explaining the robustness of the classifier and revealing potential targets for further therapeutic investigation. We anticipate that our resource will build up trust in machine learning in clinical settings, foster biomarker discovery and development of compact point-of-care assays, and enable further epigenome research of brain tumors. Our interpretable framework is accessible to the research community via an interactive web application (https://hovestadtlab.shinyapps.io/shinyMNP/). | ||
| 650 | 4 | |a Cancer genomics | |
| 650 | 4 | |a CNS cancer | |
| 650 | 4 | |a Diagnostic markers | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Oncogenes | |
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