  An important problem in the analysis of high-dimensional omics data is to
identify subsets of molecular variables that are associated with a phenotype of
interest. This requires addressing the challenges of high dimensionality,
strong multicollinearity and model uncertainty. We propose a new ensemble
learning approach for improving the performance of sparse penalised regression
methods, called STructural RANDomised Selection (STRANDS). The approach, that
builds and improves upon the Random Lasso method, consists of two steps. In
both steps, we reduce dimensionality by repeated subsampling of variables. We
apply a penalised regression method to each subsampled dataset and average the
results. In the first step, subsampling is informed by variable correlation
structure, and in the second step, by variable importance measures from the
first step. STRANDS can be used with any sparse penalised regression approach
as the "base learner". Using synthetic data and real biological datasets, we
demonstrate that STRANDS typically improves upon its base learner, and that
taking account of the correlation structure in the first step can help to
improve the efficiency with which the model space may be explored.
