Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures.