Regularized ROC method for disease classification and biomarker selection with microarray data.
BACKGROUND
An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease classification. Thus there is a need for developing statistical methods that can efficiently use such high-throughput genomic data, select biomarkers with discriminant power and construct classification rules. The ROC (receiver operator characteristic) technique has been widely used in disease classification with low-dimensional biomarkers because (1) it does not assume a parametric form of the class probability as required for example in the logistic regression method; (2) it accommodates case-control designs and (3) it allows treating false positives and false negatives differently. However, due to computational difficulties, the ROC-based classification has not been used with microarray data. Moreover, the standard ROC technique does not incorporate built-in biomarker selection.


RESULTS
We propose a novel method for biomarker selection and classification using the ROC technique for microarray data. The proposed method uses a sigmoid approximation to the area under the ROC curve as the objective function for classification and the threshold gradient descent regularization method for estimation and biomarker selection. Tuning parameter selection based on the V-fold cross validation and predictive performance evaluation are also investigated. The proposed approach is demonstrated with a simulation study, the Colon data and the Estrogen data. The proposed approach yields parsimonious models with excellent classification performance.
Ma
Shuangge
S
Department of Biostatistics, University of Washington, Washington, USA.
Huang
Jian
J
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D003110:Colonic Neoplasms / Q000235:genetics; D019295:Computational Biology; D004194:Disease / Q000145:classification*; D004967:Estrogens / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005819:Genetic Markers; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012372:ROC Curve; D013269:Stochastic Processes
D004967:Estrogens; D005819:Genetic Markers