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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Bibliografiska uppgifter
I publikationen:Int J Mol Sci
Huvudupphovsmän: Tkachev, Victor, Sorokin, Maxim, Borisov, Constantin, Garazha, Andrew, Buzdin, Anton, Borisov, Nicolas
Materialtyp: Artigo
Språk:Inglês
Publicerad: MDPI 2020
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Article
Länkar:https://ncbi.nlm.nih.gov/pmc/articles/PMC7037338/
https://ncbi.nlm.nih.gov/pubmed/31979006
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/ijms21030713
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