Finding similar regions in many strings
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Distinguishing string selection problems
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Efficient approximation algorithms for the Hamming center problem
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
On the closest string and substring problems
Journal of the ACM (JACM)
A Linear-Time Algorithm for the 1-Mismatch Problem
WADS '97 Proceedings of the 5th International Workshop on Algorithms and Data Structures
Banishing Bias from Consensus Sequences
CPM '97 Proceedings of the 8th Annual Symposium on Combinatorial Pattern Matching
On the Structure of Small Motif Recognition Instances
SPIRE '08 Proceedings of the 15th International Symposium on String Processing and Information Retrieval
Exact Solutions for Closest String and Related Problems
ISAAC '01 Proceedings of the 12th International Symposium on Algorithms and Computation
More efficient algorithms for closest string and substring problems
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Swiftly computing center strings
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
On approximating string selection problems with outliers
CPM'12 Proceedings of the 23rd Annual conference on Combinatorial Pattern Matching
Configurations and minority in the string consensus problem
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
On approximating string selection problems with outliers
Theoretical Computer Science
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The problem of finding the consensus of a given set of strings is formally defined as follows: given a set of strings S = {s1, . . . sk}, and a constant d, find, if it exists, a string s*, such that the Hamming distance of s* from each of the strings does not exceed d. In this paper we study an LP relaxation for the problem. We prove an additive upper bound, depending only in the number of strings k, and randomized bounds. We show that empirical results are much better. We also compare our program with some algorithms reported in the literature, and it is shown to perform well.