UniLasso is a two-stage penalized regression procedure that leverages univariate coefficients and magnitudes to stabilize feature selection and produce sparse predictive models. We further extend the framework to incorporate external summary statistics via uniLasso ES (external scores).
UniLasso is a two-stage penalized regression procedure that leverages univariate coefficients and magnitudes to stabilize feature selection and produce sparse predictive models.
Our results demonstrate that uniLasso attains predictive performance comparable to standard Lasso while selecting substantially fewer variants, yielding sparser and potentially more interpretable models.
We further extend the framework to incorporate external summary statistics via uniLasso ES (external scores).
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UniLasso is a two-stage penalized regression procedure that leverages univariate coefficients and magnitudes to stabilize feature selection and produce sparse predictive models. We further extend the framework to incorporate external summary statistics via uniLasso ES (external scores). These signals guide the regression toward variants with prior evidence of association by informing penalty weights and sign constraints.
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