Finding most likely haplotypes in general pedigrees through parallel search with dynamic load balancing

Pacific Symposium on Biocomputing 2011, PSB 2011

Volumn , Issue , 2011, Pages 26-37

  Otten, Lars ^a   Dechter, Rina ^a  

^a 5226 Donald Bren Hall   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRANCH AND BOUNDS; BRANCH-AND-BOUND ALGORITHMS; COMMODITY HARDWARE; EXPERIMENTAL EVALUATION; HAPLOTYPES; HARD PROBLEMS; LIKELIHOOD FUNCTIONS; PARALLEL SEARCH; PARALLELIZATIONS; SUB-PROBLEMS; WORKER NODES;

ALGORITHMS; BAYESIAN NETWORKS;

LINEAR PROGRAMMING;

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; BIOLOGY; FEMALE; HAPLOTYPE; HUMAN; MALE; PEDIGREE;

ALGORITHMS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; FEMALE; HAPLOTYPES; HUMANS; MALE; MODELS, GENETIC; PEDIGREE;

EID: 84872527104     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Kento Aida, Wataru Natsume, and Yoshiaki Futakata. Distributed computing with hierarchical masterworker paradigm for parallel branch and bound algorithm. In CCGRID, pages 156-163, 2003.
2. David P. Anderson, Jeff Cobb, Eric Korpela, Matt Lebofsky, and Dan Werthimer. Seti@home: an experiment in public-resource computing. Commun. ACM, 45(11):56-61, 2002.
3. Geoffrey Chu, Christian Schulte, and Peter J. Stuckey. Confidence-based work stealing in parallel constraint programming. In Ian Gent, editor, CP, volume 5732 of Lecture Notes in Computer Science, pages 226-241, Lisbon, Portugal, September 2009. Springer-Verlag.
4. Ǵerard Cornúejols, Miroslav Karamanov, and Yanjun Li. Early estimates of the size of branch-and-bound trees. INFORMS Journal on Computing, 18(1):86-96, 2006.
5. Rina Dechter and Robert Mateescu. AND/OR search spaces for graphical models. Artif. Intell., 171(2-3):73-106, 2007.
6. Máayan Fishelson, Nickolay Dovgolevsky, and Dan Geiger. Maximum likelihood haplotyping for general pedigrees. Human Heredity, 59:41-60, 2005.
7. Bernard Gendron and Teodor Gabriel Crainic. Parallel branch-and-bound algorithms: Survey and synthesis. Operations Research, 42(6):1042-1066, 1994.
8. Ananth Grama and Vipin Kumar. State of the art in parallel search techniques for discrete optimization problems. IEEE Trans. Knowl. Data Eng., 11(1):28-35, 1999.
9. Frank Hutter, Holger H. Hoos, and Thomas Sẗutzle. Efficient stochastic local search for MPE solving. In IJCAI, pages 169-174, 2005.
10. Philip Kilby, John Slaney, Sylvie Thíebaux, and TobyWalsh. Estimating search tree size. In AAAI, pages 1014-1019. AAAI Press, 2006.
11. Uffe Kjaerulff. Triangulation of graphs-algorithms giving small total state space. Technical report, Aalborg University, 1990.
12. Donald E. Knuth. Estimating the efficiency of backtrack programs. Mathematics of Computation, 29(129):121-136, 1975.
13. Radu Marinescu and Rina Dechter. AND/OR Branch-and-Bound search for combinatorial optimization in graphical models. Artif. Intell., 173(16-17):1457-1491, 2009.
14. Nils J. Nilsson. Artificial Intelligence: A New Synthesis. Morgan Kaufmann, 1998.
15. Lars Otten and Rina Dechter. Towards parallel search for optimization in graphical models. In ISAIM, 2010.
16. Mark Silberstein, Anna Tzemach, Nickolay Dovgolevsky, Máayan Fishelson, Assaf Schuster, and Dan Geiger. Online system for faster multipoint linkage analysis via parallel execution on thousands of personal computers. American Journal of Human Genetics, 78(6):922-935, 2006.
17. Douglas Thain, Todd Tannenbaum, and Miron Livny. Distributed computing in practice: the Condor experience. Concurrency-Practice and Experience, 17(2-4):323-356, 2005.