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Keywords:

  • high dimensionality;
  • multiple times points;
  • prediction;
  • support vector machines;
  • shrinkage;
  • temporal effects

Recent studies have collected high-dimensional data longitudinally. Examples include brain images collected during different scanning sessions and time-course gene expression data. Because of the additional information learned from the temporal changes of the selected features, such longitudinal high-dimensional data, when incorporated into appropriate statistical learning techniques, are able to more accurately predict disease status or responses to a therapeutic treatment. In this article, we review recently proposed statistical learning methods dealing with longitudinal high-dimensional data. WIREs Comput Stat 2014, 6:10–18. doi: 10.1002/wics.1282

Conflict of interest: The authors have declared no conflicts of interest for this article.

For further resources related to this article, please visit the WIREs website.