  Data integration methods that analyze multiple sources of data simultaneously
can often provide more holistic insights than can separate inquiries of each
data source. Motivated by the advantages of data integration in the era of "big
data", we investigate feature selection for high-dimensional multi-view data
with mixed data types (e.g. continuous, binary, count-valued). This
heterogeneity of multi-view data poses numerous challenges for existing feature
selection methods. However, after critically examining these issues through
empirical and theoretically-guided lenses, we develop a practical solution, the
Block Randomized Adaptive Iterative Lasso (B-RAIL), which combines the
strengths of the randomized Lasso, adaptive weighting schemes, and stability
selection. B-RAIL serves as a versatile data integration method for sparse
regression and graph selection, and we demonstrate the effectiveness of B-RAIL
through extensive simulations and a case study to infer the ovarian cancer gene
regulatory network. In this case study, B-RAIL successfully identifies
well-known biomarkers associated with ovarian cancer and hints at novel
candidates for future ovarian cancer research.
