Structural transfer using EDAs: An application to multi-marker tagging SNP selection

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Santana, Roberto ^a   Mendiburu, Alexander ^a   Lozano, Jose A ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION OF DISTRIBUTION ALGORITHMS; EVOLUTIONARY OPTIMIZATIONS; HUMAN POPULATION; OPTIMIZATION PROBLEMS; REAL-WORLD PROBLEM; SELECTION PROBLEMS; SINGLE-NUCLEOTIDE POLYMORPHISMS; STRUCTURAL INFORMATION; TARGET OPTIMIZATION; TEST CASE; TRANSFER LEARNING;

OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 84866876211     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6252963     Document Type: Conference Paper

References (31)

1. P. Larrañaga and J. A. Lozano, Eds., Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Boston/Dordrecht/London: Kluwer Academic Publishers, 2002.
2. J. A. Lozano, P. Larrañaga, I. Inza, and E. Bengoetxea, Eds., Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms. Springer, 2006.
3. H. Mühlenbein and G. Paaß, "From recombination of genes to the estimation of distributions I. Binary parameters," in Parallel Problem Solving from Nature - PPSN IV, ser. Lectures Notes in Computer Science, H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, Eds., vol. 1141. Berlin: Springer, 1996, pp. 178-187.
4. R. Santana, A. Mendiburu, N. Zaitlen, E. Eskin, and J. A. Lozano, "Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms," Artificial Intelligence in Medicine, vol. 50, pp. 193-201, 2010.
5. S. Pan and Q. Yang, "A survey on transfer learning," Knowledge and Data Engineering, IEEE Transactions on, vol. 22, no. 10, pp. 1345-1359, 2010.
6. D. Xing, W. Dai, G. Xue, and Y. Yu, "Bridged refinement for transfer learning," in Proceeding of the Knowledge Discovery in Databases Conference. PKDD-2007. Springer, 2007, pp. 324-335.
7. H. Shimodaira, "Improving predictive inference under covariate shift by weighting the log-likelihood function," Journal of Statistical Planning and Inference, vol. 90, no. 2, pp. 227-244, 2000.
8. W. Dai, Q. Yang, G. Xue, and Y. Yu, "Self-taught clustering," in Proceedings of the 25th international conference on Machine learning (ICML-2008). ACM, 2008, pp. 200-207.
9. M. Taylor and P. Stone, "Transfer learning for reinforcement learning domains: A survey," The Journal of Machine Learning Research, vol. 10, pp. 1633-1685, 2009.
10. K. Leyton-Brown, E. Nudelman, and Y. Shoham, "Empirical hardness models: Methodology and a case study on combinatorial auctions," Journal of the ACM (JACM), vol. 56, no. 4, p. 22, 2009.
11. S. Satpal and S. Sarawagi, "Domain adaptation of conditional probability models via feature subsetting," in Proceeding of the Knowledge Discovery in Databases Conference. PKDD-2007. Springer, 2007, pp. 224-235.
12. S. Bickel, M. Brückner, and T. Scheffer, "Discriminative learning for differing training and test distributions," in Proceedings of the 24th international conference on Machine learning. ACM, 2007, pp. 81-88.
13. R. Caruana, "Multitask learning," Machine Learning, vol. 28, no. 1, pp. 41-75, 1997.
14. S. Thrun and L. Pratt, Learning to learn. Kluwer Academic Pub, 1998.
15. S. Ben-David and R. Schuller, "Exploiting task relatedness for multiple task learning," Learning Theory and Kernel Machines, pp. 567-580, 2003.
16. S. Baluja and S. Davies, "Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space," in Proceedings of the 14th International Conference on Machine Learning, D. H. Fisher, Ed. San Francisco, CA.: Morgan Kaufmann, 1997, pp. 30-38.
17. B. Howie, C. Carlson, M. Rieder, and D. Nickerson, "Efficient selection of tagging single-nucleotide polymorphisms in multiple populations," Human genetics, vol. 120, no. 1, pp. 58-68, 2006.
18. A. González-Neira, X. Ke, O. Lao, F. Calafell, A. Navarro, D. Comas, H. Cann, S. Bumpstead, J. Ghori, S. Hunt et al., "The portability of tagSNPs across populations: a worldwide survey," Genome research, vol. 16, no. 3, p. 323, 2006.
19. S. Gu, A. Pakstis, H. Li, W. Speed, J. Kidd, and K. Kidd, "Significant variation in haplotype block structure but conservation in tagSNP patterns among global populations," European Journal of Human Genetics, vol. 15, no. 3, pp. 302-312, 2007.
20. P. Paschou, M. Mahoney, A. Javed, J. Kidd, A. Pakstis, S. Gu, K. Kidd, and P. Drineas, "Intra-and interpopulation genotype reconstruction from tagging SNPs," Genome Research, vol. 17, no. 1, p. 96, 2007.
21. H. Mühlenbein, T. Mahnig, and A. Ochoa, "Schemata, distributions and graphical models in evolutionary optimization," Journal of Heuristics, vol. 5, no. 2, pp. 213-247, 1999.
22. A. Ochoa, M. R. Soto, R. Santana, J. Madera, and N. Jorge, "The factorized distribution algorithm and the junction tree: A learning perspective," in Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99), A. Ochoa, M. R. Soto, and R. Santana, Eds. Havana, Cuba: Editorial Academia. Havana, Cuba, March 1999, pp. 368-377.
23. S. Baluja, "Incorporating a priori knowledge in probabilistic-model based optimization," in Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications, ser. Studies in Computational Intelligence, M. Pelikan, K. Sastry, and E. Cantú-Paz, Eds. Springer, 2006, pp. 205-222.
24. R. Santana, P. Larrañaga, and J. A. Lozano, "The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms," in Proceedings of the Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, ser. Lecture Notes in Computer Science, E. Marchiori, J. H. Moore, and J. C. Rajapakse, Eds., vol. 4447. Valencia, Spain: Springer, 2007, pp. 247-257.
25. M. Hauschild, M. Pelikan, K. Sastry, and D. E. Goldberg, "Using previous models to bias structural learning in the hierarchical BOA," in Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2008. New York, NY, USA: ACM, 2008, pp. 415-422.
26. R. Santana, C. Bielza, and P. Larrañaga, "Network measures for re-using problem information in EDAs," Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid, Tech. Rep. UPM-FI/DIA/2010-3, June 2010.
27. R. Santana, R. Armañanzas, C. Bielza, and P. Larrañaga, "Network measures for information extraction and transfer learning in evolutionary algorithms," IEEE Transactions on Evolutionary Computation, 2011, submmitted to publication.
28. T. Pemberton, M. Jakobsson, D. Conrad, G. Coop, J. Wall, J. Pritchard, P. Patel, and N. Rosenberg, "Using population mixtures to optimize the utility of genomic databases: linkage disequilibrium and association study design in India," Annals of human genetics, vol. 72, no. 4, pp. 535-546, 2008.
29. H. Cann, C. de Toma, L. Cazes, M. Legrand, V. Morel, L. Piouffre, J. Bodmer, W. Bodmer, B. Bonne-Tamir, A. Cambon-Thomsen et al., "A human genome diversity cell line panel," Science, vol. 296, no. 5566, p. 261, 2002.
30. N. Rosenberg, S. Mahajan, C. Gonzalez-Quevedo, M. Blum, L. Nino-Rosales, V. Ninis, P. Das, M. Hegde, L. Molinari, G. Zapata et al., "Low levels of genetic divergence across geographically and linguistically diverse populations from India," PLoS genetics, vol. 2, no. 12, p. e215, 2006.
31. The International HapMap Consortium, "A second generation human haplotype map of over 3.1 million snps," Nature, vol. 449, no. 7164, pp. 851-861, 2007.