• A variable selection approach for highly correlated predictors in high-dimensional genomic data

    9 days ago - By Oxford Journals

    AbstractMotivationIn genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings.ResultsWe propose a novel variable selection approach called WLasso, taking these correlations into account. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the biomarkers and in applying the generalized Lasso criterion. The...
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