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...

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Detalles Bibliográficos
Autores principales: Mondal, Rahul, Do, Minh Dung, Ahmed, Nasim Uddin, Walke, Daniel, Micheel, Daniel, Broneske, David, Saake, Gunter, Heyer, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
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

Internet

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

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