Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, we address this issue as a consistency problem. We propose a new concept of performance-based consistency and a new novel gene selection method, Genetic Algorithm Gene Selection method in terms of consistency (GAGSc).

The proposed consistency concept and GAGSc method were investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy. More importantly, GAGSc has demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.