String barcoding: uncovering optimal virus signatures
Proceedings of the sixth annual international conference on Computational biology
Rapid Large-Scale Oligonucleotide Selection for Microarrays
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Group Testing With DNA Chips: Generating Designs and Decoding Experiments
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Fast and Sensitive Probe Selection for DNA Chips Using Jumps in Matching Statistics
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Probe Selection with Fault Tolerance
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Sequential Forward Selection Approach to the Non-unique Oligonucleotide Probe Selection Problem
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
An Evolutionary Approach to the Non-unique Oligonucleotide Probe Selection Problem
Transactions on Computational Systems Biology X
Bayesian Optimization Algorithm for the Non-unique Oligonucleotide Probe Selection Problem
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Pattern Recognition Letters
Space pruning monotonic search for the non-unique probe selection problem
International Journal of Bioinformatics Research and Applications
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In addition to their prevalent use for analyzing gene expression, DNA microarrays are an efficient tool for biological, medical, and industrial applications because of their ability to assess the presence or absence of biological agents, the targets, in a sample. Given a collection of genetic sequences of targets one faces the challenge of finding short oligonucleotides, the probes, which allow detection of targets in a sample by hybridization experiments. The experiments are conducted using either unique or non-unique probes, and the problem at hand is to compute a minimal design, i.e., a minimal set of probes that allows to infer the targets in the sample from the hybridization results. If we allow to test for more than one target in the sample, the design of the probe set becomes difficult in the case of non-unique probes. Building upon previous work on group testing for microarrays we describe the first approach to select a minimal probe set for the case of non-unique probes in the presence of a small number of multiple targets in the sample. The approach is based on an integer linear programming formulation and a branch-and-cut algorithm. Our implementation significantly reduces the number of probes needed while preserving the decoding capabilities of existing approaches.