Feature selection and classification for high-dimensional biological data under cross-validation framework

With the advent of high throughput technologies, the high-dimensional datasets are increasingly available. This has not only opened up new insight but also posed analytical challenges. One important problem is selecting the informative feature subset and predicting the future outcome. We propose a two-step framework for feature selection and classification model construction, which utilizes a nested and repeated cross-validation method. We evaluated our approach using both simulated data and publicly available gene expression datasets. The proposed method showed comparatively better predictive accuracy for new cases than the K-fold cross-validation method.