Ant colony optimisation of decision trees for the detection of gene-gene interactions

Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014

Volumn , Issue , 2014, Pages 57-61

  Sapin, E ^a   Keedwell, E ^a   Frayling, T ^a  

^a UNIVERSITY OF EXETER MEDICAL SCHOOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84922814163     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2014.6999248     Document Type: Conference Paper

References (33)

1. J. Bell, N. Timpson, N. Rayner, and et al. Genome-wide association scan allowing for epistasis in type 2 diabetes. Ann Hum Genet., 75:10-9, 2011.
2. L. Breiman. Random forests. Machine Learning, 45:5-32, 2001.
3. A. Bureau, J. Dupuis, B. Hayward, K. Falls, and P. Van Eerdewegh. Mapping complex traits using random forests. BMC Genetics, 4, 2003.
4. Y. Chang, Y. Chiu, P. Liu, K. Shih, M. Lin, W. Sheu, T. Quertermous, J. Curb, C. Hsiung, W. Lee, P. Lee, Y. Chen, and L. Chuang. Replication of genome-wide association signals of type 2 diabetes in han chinese in a prospective cohort. Clin Endocrinol., 76:365-72, 2012.
5. J. Christmas, E. Keedwell, T. Frayling, and J. Perry. Ant colony optimisation to identify genetic variant association with type 2 diabetes,. In Information Sciences., volume 181, pages 1609-1622, 2011.
6. L. Chuang, M. Lin, and C. Yang. Improved branch and bound algorithm for detecting snp-snp interactions in breast cancer. Journal of Clinical Bioinformatics, 3.
7. W. T. C. C. Consortium. Genome-wide association study of 14000 cases of seven common diseases and 3000 shared controls. Nature, 447:661-978, 2007.
8. R. Culverhouse, T. Klein, and W. Shannon. Detecting epistatic interactions contributing to quantitative traits genet. Epidemiol Genome Research, 27:141-152, 2004.
9. P. Diwakar, R. Rahul, S. Prashant, K. Bhaskar, and V. JK. Random forests. Combinatorial Chemistry & High Throughput Screening, 12:507.
10. T. Frayling, N. Timpson, M. Weedon, E. Zeggini, R. Freathy, C. Lindgren, J. Perry, K. Elliott, H. Lango, N. Rayner, B. Shields, L. Harries, J. Barrett, S. Ellard, C. Groves, B. Knight, A. Patch, and et al. A common variant in the fto gene is associated with body mass index and predisposes to childhood and adult obesity. Science, 316:889-94, 2007.
11. B. Goldstein, A. Hubbard, A. Cutler, and L. Barcellos. An application of random forests to a genome-wide association dataset: Methodological considerations & new findings. BMC Genetics, 11, 2010.
12. C. Greene, B. White, and J. Moore. Ant colony optimization for genome-wide genetic analysis. In M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stutzle, and A. Winfield, editors, Ant Colony Optimization and Swarm Intelligence, volume 5217 of Lecture Notes in Computer Science, pages 37-47. Springer Berlin / Heidelberg, 2008.
13. F. Gunther, N. Wawro, and K. Bammann. Neural networks for modeling gene-gene interactions in association studies. BMC Genetics., 10.
14. L. Hahn, M. Ritchie, and J. Moore. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics, 19:376-382, 2003.
15. X. He, C. Fuller, Y. Song, G. Meng, B. Zhang, X. Yang, and H. Li. Sherlock: Detecting gene-disease associations by matching patterns of expression qtl and gwas. AJHG., 92:667-680, 2013.
16. D. Hosmer and S. Lemeshow. Applied logistic regression. New York: John Wiley & Sons, Inc., 2000.
17. T. Jin and L. Liu. The wnt signaling pathway effector tcf7l2 and type 2 diabetes mellitus. Mol Endocrinol., 22:2383-92, 2008.
18. A. Johnson and C. O'Donnell. An open access database of genomewide association results. BMC Med. Genet., 10, 2009.
19. R. Klein, C. Zeiss, E. Chew, J. Tsai, R. Sackler, C. Haynes, A. Henning, J. SanGiovanni, S. Mane, S. Mayne, M. Bracken, F. Ferris, J. Ott, C. Barnstable, and J. Hoh. Complement factor h polymorphism in agerelated macular degeneration. Science 15 April 2005, 308:385-389, 2005.
20. K. Lunetta, L. Hayward, J. Segal, and P. Van Eerdewegh. Screening large-scale association study data: exploiting interactions using random forests. BMC Genetics, 5, 2004.
21. M. Nelson, S. Kardia, R. Ferrell, and S. CF. A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Research, 11:458-470, 2001.
22. S. Oh, J. Lee, M. Kwon, B. Weir, K. Ha, and T. Park. A novel method to identify high order gene-gene interactions in genome-wide association studies: Gene-based mdr. BMC Bioinformatics., 13:S5, 2012.
23. N. Oki. On Considering Epistasis in Genome Wide Association Studies. North Carolina State University, 2012.
24. M. Park and T. Hastie. Penalized logistic regression for detecting gene interactions. Biostatistics, 9:30-50, 1980.
25. M. Ritchie, L. Hahn, N. Roodi, L. Bailey, W. Dupont, F. Parl, and J. Moore. Multifactor-dimensionality reduction reveals highorder interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet., 69:138-147, 2001.
26. E. Sapin, E. Keedwell, and T. Frayling. Subset-based aco for genome wide association study: Discovery of promising combinations,. In Proceedings of the 15th annual conference on Genetic and evolutionary computation., pages 295-302, 2013.
27. R. Saxena, B. Voight, V. Lyssenko, and et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science., 316:1331-6, 2007.
28. S. Sharma, S. Ghosh, N. Anantharaman, and V. Jayaraman. Simultaneous informative gene extraction and cancer classification using acoantminer and aco-random forests,. In Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing Volume., volume 132, pages 755-761, 2012.
29. W. Touw, J. Bayjanov, L. Overmars, L. Backus, J. Boekhorst, M. Wels, and S. van Hijum. Data mining in the life sciences with random forest: a walk in the park or lost in the jungle Briefings in Bioinformatics, 2012.
30. B. Voight, L. Scott, V. Steinthorsdottir, A. Morris, C. Dina, R. Welch, E. Zeggini, C. Huth, Y. Aulchenko, G. Thorleifsson, L. McCulloch, T. Ferreira, H. Grallert, N. Amin, G. Wu, C. Willer, S. Raychaudhuri, S. McCarroll, C. Langenberg, O. Hofmann, J. Dupuis, L. Qi, A. Segre, M. van Hoek, P. Navarro, K. Ardlie, B. Balkau, R. Benediktsson, A. Bennett, R. Blagieva, E. Boerwinkle, L. Bonnycastle, K. Bengtsson Bostrm, B. Bravenboer, S. Bumpstead, N. Burtt, G. Charpentier, P. Chines, M. Cornelis, D. Couper, G. Crawford, A. Doney, and et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet., 42:579-89, 2010.
31. S. Winham, C. Colby, R. Freimuth, X. Wang, and M. De Andrade. Snp interaction detection with random forests in high-dimensional genetic data. BMC bioinformatics, 13, 2012.
32. S. Wright. Genic and organismic selection. Evolution, 34:825-843, 1980.
33. C. Yang, Z. He, X. Wan, Q. Yang, H. Xue, and W. Yu. Snpharvester: a filtering-based approach for detecting epistatic interactions in genomewide association studies. Bioinformatics, 25:504-511, 2009.