Bagging classification tree-based robust variable selection for radial basis function network modeling in metabonomics data analysis

Complex datasets can be routinely produced from modern analytical platforms in metabonomics surveys, which brings enormous challenges to existing chemometrics tools. In the current study, inspired by the characteristic of classification tree (CT) in automatically selecting the most informative variables and measuring their importance, the potential of bagging in improving the reliability and robustness of a single model, and the promising modeling performance of radial basis function network (RBFN), we designed a new chemometrics tool, i.e., bagging classification tree-radial basis function network (BAGCT-EBFN), for metabonomics data analysis. In BAGCT-RBFN, a series of parallel CT models were firstly established based on the idea of bagging (BAGCT). The informative variables can be successfully spied via inspecting the variable importance values over all CTs in BAGCT. Then, RBFN was utilized to relate the identified informative variables to the class memberships. To demonstrate the practical application of BAGCT-RBFN in metabonomics, an H-1 NMR-based metabonomics dataset associated with lung cancer was applied. The results showed that BAGCT-RBFN can find a shortlist of discriminatory variables with reliability while attain more satisfactory classification accuracy than traditional CT and RBFN.