Evolution strategy with greedy probe selection heuristics for the non-unique oligonucleotide probe selection problem

In order to accurately measure the gene expression levels in microarray experiments, it is crucial to design unique, highly specific and highly sensitive oligonucleotide probes for the identification of biological agents such as genes in a sample. Unique probes are difficult to obtain for closely related genes such as the known strains of HIV genes. The non-unique probe selection problem is to find a smallest probe set that is able to uniquely identify targets in a biological sample. This is an NP-hard problem. We present two approaches for finding near-minimal non-unique probe sets. Each approach combines of a deterministic greedy probe selection heuristic that selects good probes, with an evolution strategy that optimizes the selected probe sets. The heuristics, guided by selection functions defined over a probe set, decide at each moment which probes are the best to be included in, or excluded from, a candidate solution. Our methods produce results that are very close to, and in many cases better than, those of the current state-of-the-art approaches for the non-unique probe selection problem, namely integer linear programming, optimal cutting-plane and genetic algorithm approaches.