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Decision tree learning in Neo4j on homogeneous and unconnected graph nodes from biological and clinical datasets
BACKGROUND: Graph databases enable efficient storage of heterogeneous, highly-interlinked data, such as clinical data. Subsequently, researchers can extract relevant features from these datasets and apply machine learning for diagnosis, biomarker discovery, or understanding pathogenesis. METHODS: To...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988195/ https://www.ncbi.nlm.nih.gov/pubmed/36879243 http://dx.doi.org/10.1186/s12911-023-02112-8 |