Note: On the complexity of non-unique probe selection
Theoretical Computer Science
A Heuristic Method for Selecting Support Features from Large Datasets
AAIM '07 Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management
Selecting Genotyping Oligo Probes Via Logical Analysis of Data
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Faster Algorithm for the Set Variant of the String Barcoding Problem
CPM '08 Proceedings of the 19th annual symposium on Combinatorial Pattern Matching
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
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
Two challenges in genomics that can benefit from petascale platforms
Euro-Par'06 Proceedings of the CoreGRID 2006, UNICORE Summit 2006, Petascale Computational Biology and Bioinformatics conference on Parallel processing
Pattern Recognition Letters
A DIAMOND method of inducing classification rules for biological data
Computers in Biology and Medicine
Selecting Oligonucleotide Probes for Whole-Genome Tiling Arrays with a Cross-Hybridization Potential
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Combinatorial search on graphs motivated by bioinformatics applications: a brief survey
WG'05 Proceedings of the 31st international conference on Graph-Theoretic Concepts in Computer Science
Space pruning monotonic search for the non-unique probe selection problem
International Journal of Bioinformatics Research and Applications
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Motivation: Besides their prevalent use for analyzing gene expression, microarrays are an efficient tool for biological, medical and industrial applications due to 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. Each hybridization experiment determines whether the probe binds to its corresponding sequence in the target. Depending on the problem, the experiments are conducted using either unique or non-unique probes and usually assume that only one target is present in the sample. The problem at hand is to compute a design, i.e. a minimal set of probes that allows to infer the targets in the sample from the result of the hybridization experiment. 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. Results: 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 ILP formulation and a branch-and-cut algorithm. Our preliminary implementation greatly reduces the number of probes needed while preserving the decoding capabilities. Availability: http://www.inf.fu-berlin.de/inst/ag-bio