Feature selection using a random forests classifier for the integrated analysis of multiple data types

Complex clinical phenotypes arise from the concerted interactions among the myriad components of a biological system. Therefore, comprehensive models can only be developed through the integrated study of multiple types of experimental data gathered from the system in question. The Random ForestsTM (RF) method is adept at identifying relevant features having only slight main effects in high-dimensional data. This method is well-suited to integrated analysis, as relevant attributes may be selected from categorical or continuous data, and there may be interactions across data types. RF is a natural approach for studying gene-gene, gene-protein, or protein-protein interactions because importance scores for particular attributes take interactions into account. Thus, Rando