Machine Learning
Survival analysis of longitudinal microarrays
Bioinformatics
An overview on the shrinkage properties of partial least squares regression
Computational Statistics
Bioinformatics
Survival analysis of microarray expression data by transformation models
Computational Biology and Chemistry
Comparison of tree-based methods for prognostic stratification of survival data
Artificial Intelligence in Medicine
Computational Statistics & Data Analysis
A two-component Weibull mixture to model early and late mortality in a Bayesian framework
Computational Statistics & Data Analysis
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A new variable selection approach using Random Forests
Computational Statistics & Data Analysis
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Knowledge of transcription of the human genome might greatly enhance our understanding of cancer. In particular, gene expression may be used to predict the survival of cancer patients. Microarray data are characterized by their high-dimensionality: the number of covariates (p~1000) greatly exceeds the number of samples (n~100), which is a considerable challenge in the context of survival prediction. An inventory of methods that have been used to model survival using gene expression is given. These methods are critically reviewed and compared in a qualitative way. Next, these methods are applied to three real-life data sets for a quantitative comparison. The choice of the evaluation measure of predictive performance is crucial for the selection of the best method. Depending on the evaluation measure, either the L"2-penalized Cox regression or the random forest ensemble method yields the best survival time prediction using the considered gene expression data sets. Consensus on the best evaluation measure of predictive performance is needed.