Approximation algorithms of non‐unique probes selection for biological target identification

Non‐unique probes are used to identify the targets, i.e., viruses, present in a given sample. Since the number of selected non‐unique probes is equal to the number of hybridization experiments, it is important to find a minimum set of non‐unique probes, which is NP‐complete. Using d‐disjunct matrix, we present two (1+(d+1)logn)‐approximation algorithms to identify at most d targets. Based on our selected non‐unique probes, we also present the decoding algorithms with linear time complexity. In addition, our solutions are fault tolerant. The proposed algorithms can identify at most d targets in the presence of experimental errors.

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