TY - JOUR
T1 - Survival prediction using gene expression: a review and comparison
AU - van Wieringen, W.N.
AU - Kun, D.
AU - Hampel, R.
AU - Boulesteix, A.-L.
PY - 2009
Y1 - 2009
N2 - 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
AB - 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
U2 - https://doi.org/10.1016/j.csda.2008.05.021
DO - https://doi.org/10.1016/j.csda.2008.05.021
M3 - Article
SN - 0167-9473
VL - 53
SP - 1590
EP - 1603
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 5
ER -
