Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions

IET Systems Biology

Volumn 9, Issue 6, 2015, Pages 218-225

  Sapin, Emmanuel ^a   Keedwell, Ed ^a   Frayling, Tim ^b  

^a UNIVERSITY OF EXETER   (United Kingdom)

^b UNIVERSITY OF EXETER MEDICAL SCHOOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA MINING; DECISION TABLES; DECISION TREES; GENES; OPTIMIZATION; TREES (MATHEMATICS);

ACO ALGORITHMS; CONTINGENCY TABLE; GENE-GENE INTERACTION; GENOME-WIDE ASSOCIATION STUDIES; NEAR-OPTIMAL; SINGLE NUCLEOTIDE POLYMORPHISMS; TYPE II DIABETES;

ANT COLONY OPTIMIZATION;

ALGORITHM; ANIMAL; ANT; BIOLOGICAL MODEL; DECISION SUPPORT SYSTEM; EPISTASIS; HUMAN; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; ANIMALS; ANTS; DECISION SUPPORT TECHNIQUES; EPISTASIS, GENETIC; HUMANS; MODELS, GENETIC; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84948743924     PISSN: 17518849     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-syb.2015.0017     Document Type: Article

References (46)

1. Klein, R., Zeiss, C., Chew, E., et al. 'Complement factor h polymorphism in age-related macular degeneration', Sci. 15 April 2005, 2005, 308, pp. 385-389.
2. Johnson, A., O'Donnell, C.: 'An open access database of genome-wide association results', BMC Med. Genet., 2009, 10, (1), 6.
3. Wright, S.: 'Genic and organismic selection', Evolution, 1980, 34, pp. 825-843.
4. Oki, N.: 'On considering epistasis in genome wide association studies' (North Carolina State University, North Carolina, USA 2012).
5. TheWellcome Trust Case Control Consortium: 'Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls', Nature, 2007, 447, pp. 661-978.
6. Bureau, A., Dupuis, J., Hayward, B., Falls, K., Van Eerdewegh, P.: 'Mapping complex traits using random forests', BMC Genet., 2003, 4, (Suppl 1), S64. doi:10.1186/1471-2156-4-S1-S64.
7. Goldstein, B., Hubbard, A., Cutler, A., Barcellos, L.: 'An application of random forests to a genome-wide association dataset: methodological considerations & new findings', BMC Genet., 2010, 11, 49. doi:10.1186/1471-2156-11-49.
8. Lunetta, K., Hayward, L., Segal, J., Van Eerdewegh, P.: 'Screening large-scale association study data: exploiting interactions using random forests', BMC Genet., 2004, 5, 32. doi:10.1186/1471-2156-5-32.
9. Touw, W., Bayjanov, J., Overmars, L., et al.: 'Data mining in the life sciences with random forest: a walk in the park or lost in the jungle?', Brief. Bioinform., 2013, 14, (3), 315-326. doi: 10.1093/bib/bbs034.
10. Winham, S., Colby, C., Freimuth, R., Wang, X., De Andrade, M.: 'SNP interaction detection with random forests in high-dimensional genetic data', BMC Bioinformatics, 2012, 13.
11. Hahn, L., Ritchie, M., Moore, J.: 'Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions', Bioinformatics, 2003, 19, pp. 376-382.
12. Ritchie, M., Hahn, L., Roodi, N., et al.: 'Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer', Am. J. Hum. Genet., 2001, 69, pp. 138-147.
13. Nelson, M., Kardia, S., Ferrell, R., Sing, C.F.: 'A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation', Genome Res., 2001, 11, pp. 458-470.
14. Culverhouse, R., Klein, T., Shannon, W.: 'Detecting epistatic interactions contributing to quantitative traits genet', Epidemiol. Genome Res., 2004, 27, pp. 141-152.
15. Hosmer, D., Lemeshow, S.: 'Applied logistic regression' (John Wiley & Sons, Inc., New York, 2000).
16. Park, M., Hastie, T.: 'Penalized logistic regression for detecting gene interactions', Biostatistics, 1980, 9, pp. 30-50.
17. Voight, B.F., Scott, L.J., Steinthorsdottir, V., et al.: 'Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis', Nat. Genet., 2010, 42, pp. 579-589.
18. Gunther, F., Wawro, N., Bammann, K.: 'Neural networks for modeling gene-gene interactions in association studies', BMC Genet., 2009, 10, 87. doi:10.1186/1471-2156-10-87.
19. Chuang, L., Lin, M., Yang, C.: 'Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer', J. Clin. Bioinform., 2013, 3, 4. doi:10.1186/2043-9113-3-4.
20. Yang, C., He, Z., Wan, X., Yang, Q., Xue, H., Yu, W.: 'SNPharvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies', Bioinformatics, 2009, 25, pp. 504-511.
21. Mooney, M., Wilmot, B., Bipolar Genome Study, McWeeney, T.S., et al.: 'The GA and the GWAS: using genetic algorithms to search for multi-locus associations'. IEEE/ACM Transactions on Computational Biology and Bioinformatics/IEEE, 2011, doi: 10.1109/TCBB. 2011.145. PMC. Web. 16 Apr. 2015.
22. Moore, J.H., White, W.C.: 'Exploiting knowledge in genetic programming for genome-wide genetic analysis'. Parallel Problem Solving from Nature - PPSN IX, 2010 (LNCS, 4193), pp. 969-977.
23. Greene, C., White, B., Moore, J.: 'Ant colony optimization for genome-wide genetic analysis'. Ant Colony Optimization and Swarm Intelligence, Berlin/Heidelberg, 2008 (LNCS, 5217), pp. 37-47.
24. Shang, J., Zhang, J., Lei, X., Zhang, Y., Chen, B.: 'Incorporating heuristic information into ant colony optimization for epistasis detection', 2012, 34, (3), pp. 321-327.
25. Zhou, Z., Liu, G., Su, L.: 'A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework', Int. J. Comput. Math., 2015, pp. 1-13.
26. Wang, Y., Liu, X., Robbins, K., Rekaya, R.: 'AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm', BMC Res. Notes, 2010, 3, p. 117.
27. Christmas, J., Keedwell, E., Frayling, T.M., Perry, J.R.B.: 'Ant colony optimisation to identify genetic variant association with type 2 diabetes', Inf. Sci., 2011, 181, pp. 1609-1622.
28. Sapin, E., Keedwell, E., Frayling, T.: 'Subset-based ant colony optimisation for the discovery of gene-gene interactions in genome wide association studies'. Proc. 15th Annual Conf. on Genetic and Evolutionary Computation, 2013, pp. 295-302.
29. Sapin, E., Keedwell, E., Frayling, T.: 'Ant colony optimisation for exploring logical gene-gene associations in genome wide association studies'. IWBBIO'13, 2013, pp. 449-456.
30. Sapin, E., Keedweel, E., Frayling, T.: 'Subset-based ant colony optimisation of decision trees for the detection of gene-gene interactions'. 2014 IEEE International Conference on Bioinformatics and Biomeicine (BIBM), Nov. 2014, pp 57-61.
31. Breiman, L.: 'Random forests', Mach. Learn., 2001, 45, pp. 5-32.
32. Sharma, S., Ghosh, S., Anantharaman, N., Jayaraman, V.: 'Simultaneous informative gene extraction and cancer classification using aco-antminer and aco-random forests'. Proc. Int. Conf. on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing Volume, 2012, vol. 132, pp. 755-761.
33. Diwakar, P., Rahul, R., Prashant, S., Bhaskar, K., Jayaraman, V.K.: 'Random forests', Comb. Chem. High Throughput Screen., 12, p. 507.
34. Boryczka, U., Kozak, J.: 'New insights of cooperation among ants in ant colony decision trees'. 2011 Third World Congress on IEEE Nature and Biologically Inspired Computing (NaBIC), 2011, pp. 255-260.
35. Boryczka, U., Kozak, J.: 'Ant colony decision trees - a new method for constructing decision trees based on ant colony optimization'. Computational Collective Intelligence. Technologies and Applications, 2010 (LNCS, 6421), pp. 373-382.
36. Otero, F., Freitas, A., Johnson, C.: 'Inducing decision trees with an ant colony optimization algorithm', Appl. Soft Comput., 2012, 12, (11), pp. 3615-3626.
37. Oh, S., Lee, J., Kwon, M., Weir, B., Ha, K., Park, T.: 'A novel method to identify high order gene-gene interactions in genome-wide association studies: gene-based MDR', BMC Bioinform., 2012, 13, p. S5.
38. Dorigo, M., Maniezzo, V., Colorni, A.: 'Positive feedback as a search strategy'. Technical Report, no. 91-016, Politecnico di Milano, Italy, 1991.
39. Sapin, E., Keedwell, E.: 'T-ACO - tournament ant colony optimisation for high-dimensional problems'. Proc. of the Int. Joint Conf. on Computational Intelligence, 2012, pp. 81-86.
40. Sapin, E., Keedweel, E. 'A subset-based ant colony optimisation with tournament path selection for high-dimensional problem'. Transactions on Computional Collective Intelligence (LNCS, 8790), 17, pp 232-247.
41. Leguizamon, G., Michalewicz, Z.: 'A new version of ant system for subset problems'. Proc. of the 1999 Congress on Evolutionary Computation (CEC 99), 1999, vol. 2, pp. 1458-1464.
42. Saxena, R., Voight, B.F., Lyssenko, V., et al.: 'Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels', Science, 2007, 316, pp. 1331-1336.
43. Jin, T., Liu, L.: 'The wnt signaling pathway effector TCF7l2 and type 2 diabetes mellitus', Mol. Endocrinol., 2008, 22, pp. 2383-2392.
44. Bell, J., Timpson, N.J., Rayner, N.W., et al.: 'Genome-wide association scan allowing for epistasis in type 2 diabetes', Ann. Hum. Genet., 2011, 75, pp. 10-19.
45. He, X., Fuller, C., Song, Y., et al.: 'Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAA', AJHG, 2013, 92, pp. 667-680.
46. Chang, Y., Liu, P., Shih, K., et al.: 'Replication of genome-wide association signals of type 2 diabetes in han Chinese in a prospective cohort', Clin. Endocrinol., 2012, 76, pp. 365-372.