Abstract
In order to capitalize on the extensive biological research publications and databases, knowledge graphs can help extract clinically useful details from large and complicated resources. Here, we compare utility of knowledge graphs and named entity extraction for identifying biologically appropriate results from breast cancer subtyping publications. This biomedical field is an excellent representative test set - the biological mechanisms are well studied but complex, while the clinical applications of identifying breast cancer subtypes are critical to making appropriate diagnostic and therapeutic considerations. Optimizing knowledge graphs to extract actionable biological details rapidly and accurately could have huge implications in translating biological data into clinical care responses. Our research suggests that limitations exist in current knowledge graph pipelines in biomedical data analysis, primarily related to named entity extraction issues.
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Davidson, J. et al. (2022). Comparisons of Knowledge Graphs and Entity Extraction in Breast Cancer Subtyping Biomedical Text Analysis. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_21
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