Mistakes in validating the accuracy of a prediction classifier in high-dimensional but small-sample microarray data.
A major interest in gene expression microarray studies is to develop an accurate classifier which can be adopted in clinical practice. The usage of large numbers of genes with small data samples may lead to overfitting in classification, and generate promising, but often nonreproducible results. Therefore, assessing the reproducibility of a classifier is necessary. Appropriate methods for validating a developed classifier and estimating its predicting accuracy are discussed. In addition, some mistakes that can arise in the cross validation process are reviewed using published articles in prominent medical journals, to prevent the indefinite results of a classifier development from leading to inappropriate treatment.
Lee
Sunho
S
Department of Applied Mathematics, Sejong University, Seoul, South Korea. leesh@sejong.ac.kr
D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics; D003627:Data Interpretation, Statistical; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D018398:Homeodomain Proteins / Q000235:genetics; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D018123:Receptors, Interleukin / Q000235:genetics; D053722:Receptors, Interleukin-17; D013629:Tamoxifen / Q000627:therapeutic use
D018931:Antineoplastic Agents, Hormonal; C484414:HOXB13 protein, human; D018398:Homeodomain Proteins; C408922:IL17RB protein, human; D018123:Receptors, Interleukin; D053722:Receptors, Interleukin-17; D013629:Tamoxifen