RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Extremal optimization: heuristics via coevolutionary avalanches
Computing in Science and Engineering
Structural alignment of large—size proteins via lagrangian relaxation
Proceedings of the sixth annual international conference on Computational biology
Mining Residue Contacts in Proteins Using Local Structure Predictions
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
Algorithmic aspects of protein folding and protein structure similarity
Algorithmic aspects of protein folding and protein structure similarity
Optimal Protein Structure Alignment Using Maximum Cliques
Operations Research
Text similarity: an alternative way to search MEDLINE
Bioinformatics
A branch-and-reduce algorithm for the contact map overlap problem
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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This paper proposes a similarity matrix-based hybrid algorithm for the contact map overlap (CMO) problem in protein structure alignment. In this algorithm, Genetic Algorithm (GA) is used as a framework, in which the initial solutions are constructed with similarity matrix heuristic, and Extremal Optimization (EO) is embedded as a mutated operator. In this process, EO quickly approaches near-optimal solutions and GA generates improved global approximations. Five similarity measurements including ratio, inner product, cosine function, Jaccard index and Dice coefficient have been exploited to compute the similarity matrix between two contact maps. The simulations demonstrate that our algorithm is significantly faster and gets better results for most of the test sets.