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          <dc:title>&lt;p&gt;Lasso sign changes between univariate and multivariate models. This table displays the number of coefficient sign changes between the univariate and multivariate models for each phenotype and the percentage of coefficients in the active-set that switched. UniLasso, by construction, eliminates all such sign reversals; every non-zero coefficient retains the sign of its univariate effect.&lt;/p&gt;</dc:title>
          <dc:creator>Joshua Richland (25099428)</dc:creator>
          <dc:creator>Tuomo Kiiskinen (8704011)</dc:creator>
          <dc:creator>William Wang (5811092)</dc:creator>
          <dc:creator>Wenhui Sophia Lu (25099431)</dc:creator>
          <dc:creator>Balasubramanian Narasimhan (6444833)</dc:creator>
          <dc:creator>Trevor Hastie (195)</dc:creator>
          <dc:creator>Manuel Rivas (268944)</dc:creator>
          <dc:creator>Robert Tibshirani (196)</dc:creator>
          <dc:subject>Genetics</dc:subject>
          <dc:subject>Evolutionary Biology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Mathematical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Science Policy</dc:subject>
          <dc:subject>Infectious Diseases</dc:subject>
          <dc:subject>stabilize feature selection</dc:subject>
          <dc:subject>regression toward variants</dc:subject>
          <dc:subject>level target data</dc:subject>
          <dc:subject>informing penalty weights</dc:subject>
          <dc:subject>guided sparse regression</dc:subject>
          <dc:subject>based repository comprising</dc:subject>
          <dc:subject>external statistics guide</dc:subject>
          <dc:subject>external scores ).</dc:subject>
          <dc:subject>recently introduced univariate</dc:subject>
          <dc:subject>leverages univariate coefficients</dc:subject>
          <dc:subject>prs estimation methods</dc:subject>
          <dc:subject>signals guide</dc:subject>
          <dc:subject>unilasso ).</dc:subject>
          <dc:subject>yielding sparser</dc:subject>
          <dc:subject>xlink "&gt;</dc:subject>
          <dc:subject>united kingdom</dc:subject>
          <dc:subject>standard lasso</dc:subject>
          <dc:subject>sign constraints</dc:subject>
          <dc:subject>results demonstrate</dc:subject>
          <dc:subject>remains competitive</dc:subject>
          <dc:subject>prior evidence</dc:subject>
          <dc:subject>interpretable models</dc:subject>
          <dc:subject>empirical advantages</dc:subject>
          <dc:description>&lt;p&gt;Lasso sign changes between univariate and multivariate models. This table displays the number of coefficient sign changes between the univariate and multivariate models for each phenotype and the percentage of coefficients in the active-set that switched. UniLasso, by construction, eliminates all such sign reversals; every non-zero coefficient retains the sign of its univariate effect.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-23T17:43:15Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pgen.1012314.t002</dc:identifier>
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