Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we prop...

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Detalles Bibliográficos
Autores principales: Tkachev, Victor, Sorokin, Maxim, Borisov, Constantin, Garazha, Andrew, Buzdin, Anton, Borisov, Nicolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037338/
https://www.ncbi.nlm.nih.gov/pubmed/31979006
http://dx.doi.org/10.3390/ijms21030713

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037338/
https://www.ncbi.nlm.nih.gov/pubmed/31979006
http://dx.doi.org/10.3390/ijms21030713

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