Optimized application of penalized regression methods to diverse genomic data

  title={Optimized application of penalized regression methods to diverse genomic data},
  author={Levi Waldron and Melania Pintilie and Ming-Sound Tsao and Frances A. Shepherd and Curtis Huttenhower and Igor Jurisica},
MOTIVATION Penalized regression methods have been adopted widely for high-dimensional feature selection and prediction in many bioinformatic and biostatistical contexts. While their theoretical properties are well-understood, specific methodology for their optimal application to genomic data has not been determined. RESULTS Through simulation of contrasting scenarios of correlated high-dimensional survival data, we compared the LASSO, Ridge and Elastic Net penalties for prediction and… CONTINUE READING
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Applied survival analysis: regression modeling of time to event data

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