High-level Biomedical Data Integration in a Semantic Knowledge Graph with OncodashKB for finding Personalized Actionable Drugs in Ovarian Cancer Article Swipe
Johann Dréo
,
Sebastian Lobentanzer
,
E. Gaydukova
,
Marko Barić
,
Altti Ilari Maarala
,
Taru Muranen
,
Jaana Oikkonen
,
Federico Bolelli
,
Vittorio Pipoli
,
Veli‐Matti Isoviita
,
Johanna Hynninen
,
Benno Schwikowski
·
YOU?
·
· 2024
· Open Access
·
YOU?
·
· 2024
· Open Access
·
Background: The growing amount of biomedical knowledge about cancer in combination with genome-scale patient profiling data offers unprecedented opportunities for personalized oncology. However, the large amounts of knowledge and data require scalable approaches to providing actionable information to support clinicians in decision-making [1]. \n \nObjective: To develop software and methods that integrate all relevant clinical and genomic data about patients and that enable the discovery of optimal personalized treatment options, together with the supporting literature knowledge and data. \n \nMethods: We exploit a Semantic Knowledge Graph (SKG), a type of database that represents medical data in the form of objects and relationships, linking previously unconnected information across several cancer databases. To build up this SKG (OncodashKB), we use the BioCypher library [2]. We then integrate clinical data from patients with high-grade serous ovarian cancer, including information on genome changes collected as part of the DECIDER project (http://deciderproject.eu). The SKG can then be queried to gather evidence paths linking patient-specific alterations to actionable drugs. \n \nResults: Our approach provides a fully automated, systematic, and reproducible data integration workflow, along with the use of existing expert-made ontologies to provide interoperability and semantic descriptions. The integrated data is assessed by experts on molecular tumor boards and allows for the exploration of relevant clinical and genomic patient data in a visually accessible format, designed for ease of interpretation by clinicians. Importantly, we expect the system to reveal unexpected evidence paths between patient sequencing data and optimal treatment options based on biomedical knowledge described in the literature and confirmed by high-level evidence. \n \nConclusion: Decision support systems using graph databases emerge as valuable tools by revealing new connections between various patient data and treatment options shown in an easy-to-understand format. \n \nReferences: \n[1] Reisle, C., Williamson, L.M., Pleasance, E. et al. A platform for oncogenomic reporting and interpretation. Nat Commun 13, 756 (2022). https://doi.org/10.1038/s41467-022-28348-y \n[2] Lobentanzer, S., Aloy, P., Baumbach, J. et al. Democratizing knowledge representation with BioCypher. Nat Biotechnol 41, 1056–1059 (2023). https://doi.org/10.1038/s41587-023-01848-y.
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- Type
- article
- Language
- en
- Landing Page
- https://hal.science/hal-04509599
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392929109
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392929109Canonical identifier for this work in OpenAlex
- Title
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High-level Biomedical Data Integration in a Semantic Knowledge Graph with OncodashKB for finding Personalized Actionable Drugs in Ovarian CancerWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Johann Dréo, Sebastian Lobentanzer, E. Gaydukova, Marko Barić, Altti Ilari Maarala, Taru Muranen, Jaana Oikkonen, Federico Bolelli, Vittorio Pipoli, Veli‐Matti Isoviita, Johanna Hynninen, Benno SchwikowskiList of authors in order
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- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://hal.science/hal-04509599Direct OA link when available
- Concepts
-
Computer science, Knowledge graph, Graph, Ovarian cancer, Data integration, Information retrieval, Cancer, Medicine, Data mining, Internal medicine, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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