Determining cutoff point of ensemble trees based on sample size in predicting clinical dose with DNA microarray data

DOI10.1155/2016/6794916zbMath1423.92130OpenAlexW2563714500WikidataQ31155158 ScholiaQ31155158MaRDI QIDQ2013966

Selen Yılmaz Isıkhan, Erdem Karabulut, Celal Reha Alpar

Publication date: 10 August 2017

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/6794916

zbMATH Keywords

clinical dose predictionDNA gene expression microarray dataensemble trees

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Medical applications (general) (92C50)

Uses Software

• gbm

Cites Work

• Bagging predictors
• Boosting algorithms: regularization, prediction and model fitting
• Out-of-bag estimation of the optimal sample size in bagging
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• The elements of statistical learning. Data mining, inference, and prediction
• Stochastic gradient boosting.
• Using iterated bagging to debias regressions

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