Divide-and-Conquer Frontier Search Applied to Optimal Sequence Alignment

Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000

Volumn , Issue , 2000, Pages 910-916

  Korf, Richard E ^a   Zhang, Weixiong ^b  

^a University of California   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DNA SEQUENCES; HEURISTIC ALGORITHMS;

BI-DIRECTIONAL SEARCH; DIVIDE-AND-CONQUER; GLOBAL ALIGNMENT; HEURISTIC SEARCH; OPTIMAL SEQUENCE; PROBLEM SIZE; PROBLEM SPACE; SEQUENCE ALIGNMENTS; SHORT CYCLE; SPACE COMPLEXITY;

DYNAMIC PROGRAMMING;

EID: 0012255660     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Dijkstra, E.W., A note on two problems in connexion with graphs, Numerische Mathematik, Vol. 1, 1959, pp. 269-71.
2. Hart, P.E., N.J. Nilsson, and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on Systems Science and Cybernetics, Vol. SSC-4, No. 2, July 1968, pp. 100-107.
3. Hirschberg, D.S., A linear space algorithm for computing maximal common subsequences, Communications of the ACM, Vol. 18, No. 6, June, 1975, pp. 341-343.
4. Gupta, S.K., J.D. Kececioglu, and A.A. Schaffer, Improving the practical space and time efficiency of the shortest-paths approach to sum-of-pairs multiple sequence alignment, Journal of Computational Biology, Vol. 3, No. 2, 1995, pp. 459-472.
5. Ikeda, T., and H. Imai, Enhanced A* algorithms for multiple alignments: optimal alignments for several sequences and k-opt approximate alignments for large cases, Theoretical Computer Science, Vol. 210, No. 2, Jan. 1999, pp. 341-374.
6. Kaindl, H., and G. Kainz, Bidirectional heuristic search reconsidered, Journal of Artificial Intelligence Research, Vol. 7, 1997, pp. 283-317.
7. Korf, R.E., Depth-first iterative-deepening: An optimal admissible tree search, Artificial Intelligence, Vol. 27, No. 1, 1985, pp. 97-109.
8. Korf, R.E., Space-efficient search algorithms, Computing Surveys, Vol. 27, No. 3, Sept., 1995, pp. 337-339.
9. Korf, R.E., Divide-and-conquer bidirectional search: First results, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, August 1999, pp. 1184-1189.
10. Miura, T., and T. Ishida, Stochastic node caching for memory-bounded search, Proceedings of the National Conference on Artificial Intelligence (AAAI-98), Madison, WI, July, 1998, pp. 450-456.
11. Needleman, S.B., and C.D. Wunsch, A general method applicable to the search for similarities in the amino acid sequences of two proteins, Journal of Molecular Biology, Vol. 48, 1970, pp. 443-453.
12. Setubal, J., and J. Meidanis, Introduction to Computational Molecular Biology, PWS Publishing, Boston, MA, 1997.
13. Spouge, J.L., Speeding up dynamic programming algorithms for finding optimal lattice paths, SIAM Journal of Applied Math, Vol. 49, No. 5, 1989, pp. 1552-1566.
14. Ukkonen, E., Algorithms for approximate string matching, Information and Control, Vol. 64, 1985, pp. 100-118.