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Two-stage variable selection for molecular prediction of disease
| Content Provider | CiteSeerX |
|---|---|
| Author | Firouzi, Hamed Rajaratnam, Bala |
| Abstract | Abstract—A two-stage predictor strategy is introduced in the context of high dimensional data (large p, small n). Here the focus application is a medical one: prediction of symptomatic infection given molecular expression levels in blood. The first stage of the two-stage predictor uses the previously introduced method of Predictive Correlation Screening (PCS) to select a subset of genes that are important in the prediction of symptom scores. Selected genes are used in the second stage to learn a predictor for the prediction of symptom scores. Under sampling budget constraints we derive the optimal sample allocation rules to the first and second stages of the two-stage predictor. Superiority of the proposed predictor relative to the well known method of LASSO is shown via experiment. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Molecular Prediction Two-stage Variable Selection Second Stage Two-stage Predictor Symptom Score Budget Constraint Two-stage Predictor Strategy Focus Application Molecular Expression Level Predictive Correlation Screening High Dimensional Data Optimal Sample Allocation Rule First Stage Symptomatic Infection Predictor Relative |
| Content Type | Text |
