By Sudan M.
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Extra resources for Algorithmic Introduction to Coding Theory
6. The Skolnick set. set, we have been able to solve 55 problems optimally and for 421 problems (70 percent) the gap between the best solution found and the current upper bound was less than or equal to 5, thereby providing a strong certiﬁcate of near–optimality. The results also show the eﬀectiveness of our lower bounding heuristic procedures, and in particular, of our genetic algorithm. The GA heuristic turned out to be superior to the others, ﬁnding 52 of the 55 optimal solutions. In a second test, we used our programs to cluster proteins according to their Contact Map Overlap.
J. Barton. Multiple protein sequence alignment from tertiary structure comparison. PROTEINS: Struct. Funct. Genet. 14 (1992) 309–323 A. Sali and T. L. Blundell. Deﬁnition of general topological equivalence in protein structures. A procedure involving comparison of properties and relationships Protein Structure Comparison: Algorithms and Applications 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 33 through simulated annealing and dynamic programming. J. Mol. Biol. 212(2) (1990) 403-428 N. Siew, A. Elofsson, L.
Biol. 105 (1976) 75–95 2. F. Allen et al. Blue Gene: A vision for protein science using a petaﬂop supercomputer. IBM System Journal 40(2) (2001) 310-321 3. Bourne, The Protein Data Bank. Nucl. Ac. Res. 28 (2000) 235–242 4. T. L. Blundell. Structure-based drug design. Nature 384 (1996) 23–26 5. C. Branden and J. Tooze, Introduction to Protein Structure. Garland, 1999 6. J. M. Bujnicki, A. Elofsson, D. Fischer and L. Rychlewski. LiveBench-1: Continuous benchmarking of protein structure prediction servers.
Algorithmic Introduction to Coding Theory by Sudan M.
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