An iterative approach to probe-design for compressive sensing microarrays

The compressive sensing microarrays design was proposed by Sheikh et. al as an efficient way of sensing organisms in a given environment such as air, water or soil sample. However, Sheik et. al probe candidates are extracted from the shortest sequences among any given group of organisms. This implies that they have a limited search space for the probe candidates. Probes picked in such a way must not be the most optimal probe candidates. In this paper, we introduce an alternative compressive sensing probe picking algorithm, which consider all possible hybridization affinities and chooses the best group identifier probe among all possible probe candidates from all the members of a group. More importantly, we built relatively larger compressive sensing microarrays systems that consist of four or five groups with the total number of 22 organisms. The system that we built can sense all these organisms effectively by using a nonlinear decoding algorithm known as Belief Propagation (BP).