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Complexity
Volume 2017, Article ID 5024867, 10 pages
https://doi.org/10.1155/2017/5024867
Research Article

FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis

1Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai 201804, China
2Science Computing and Intelligent Information Processing of Guang Xi Higher Education Key Laboratory, Guangxi Teachers Education University, Nanning, Guangxi 530001, China

Correspondence should be addressed to De-Shuang Huang; nc.ude.ijgnot@gnauhsd

Received 31 March 2017; Accepted 24 July 2017; Published 7 September 2017

Academic Editor: Jianxin Wang

Copyright © 2017 Lin Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. N. Hirschhorn and M. J. Daly, “Genome-wide association studies for common diseases and complex traits,” Nature Reviews Genetics, vol. 6, no. 2, pp. 95–108, 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. B. N. Howie, P. Donnelly, and J. Marchini, “A flexible and accurate genotype imputation method for the next generation of genome-wide association studies,” PLoS Genetics, vol. 5, no. 6, Article ID e1000529, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. T. A. Manolio, F. S. Collins, N. J. Cox et al., “Finding the missing heritability of complex diseases,” Nature, vol. 461, no. 7265, pp. 747–753, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. B. S. Shastry, “SNP alleles in human disease and evolution,” Journal of Human Genetics, vol. 47, no. 11, pp. 561–566, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Stubbs, D. Vancampfort, M. De Hert, and A. J. Mitchell, “The prevalence and predictors of type two diabetes mellitus in people with schizophrenia: a systematic review and comparative meta-analysis,” Acta Psychiatrica Scandinavica, vol. 132, no. 2, pp. 144–157, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. K. P. Liao, “Cardiovascular disease in patients with rheumatoid arthritis,” Trends in Cardiovascular Medicine, vol. 27, no. 2, pp. 136–140, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Mao, N. R. London, L. Ma, D. Dvorkin, and Y. Da, “Detection of SNP epistasis effects of quantitative traits using an extended Kempthorne model,” Physiological Genomics, vol. 28, no. 1, pp. 46–52, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Zhang, J. Zhu, E. E. Schadt, and J. S. Liu, “A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules,” PLoS Computational Biology, vol. 6, no. 1, Article ID e1000642, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  9. M. Kang, C. Zhang, H.-W. Chun, C. Ding, C. Liu, and J. Gao, “EQTL epistasis: Detecting epistatic effects and inferring hierarchical relationships of genes in biological pathways,” Bioinformatics, vol. 31, no. 5, pp. 656–664, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Lin, D. Chen, P. Huang et al., “SNP interaction pattern identifier (SIPI): an intensive search for SNP–SNP interaction patterns,” Bioinformatics, 2016. View at Publisher · View at Google Scholar
  11. R. L. Prentice and L. Qi, “Aspects of the design and analysis of high-dimensional SNP studies for disease risk estimation,” Biostatistics, vol. 7, no. 3, pp. 339–354, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. S.-P. Deng, L. Zhu, and D.-S. Huang, “Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks,” BMC Genomics, vol. 16, no. 3, article no. S4, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. S.-P. Deng and D.-S. Huang, “SFAPS: An R package for structure/function analysis of protein sequences based on informational spectrum method,” Methods, vol. 69, no. 3, pp. 207–212, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. J. H. Moore, J. M. Lamb, N. J. Brown, and D. E. Vaughan, “A comparison of combinatorial partitioning and linear regression for the detection of epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms on plasma PAI-1 Levels,” Clinical Genetics, vol. 62, no. 1, pp. 74–79, 2002. View at Publisher · View at Google Scholar · View at Scopus
  15. B. M. Michael, R. E. Neapolitan, X. Jiang, and V. Shyam, “Learning genetic epistasis using Bayesian network scoring criteria,” BMC Bioinformatics, vol. 12, no. 1, 89 pages, 2011. View at Google Scholar
  16. Y. Wang, X. Liu, K. Robbins, and R. Rekaya, “AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm,” BMC Research Notes, vol. 3, article 117, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Zhang and J. S. Liu, “Bayesian inference of epistatic interactions in case-control studies,” Nature Genetics, vol. 39, no. 9, pp. 1167–1173, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Dorigo, M. Birattari, and C. Blum, “Ant colony optimization and swarm intelligence,” SpringerVerlag, vol. 5217, no. 8, pp. 767–771, 2004. View at Google Scholar
  19. T. Stützle, M. López-Ibáñez, P. Pellegrini et al., “Parameter adaptation in ant colony optimization,” Autonomous Search, vol. 9783642214349, pp. 191–215, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Blum and M. Sampels, “An ant colony optimization algorithm for shop scheduling problems,” Journal of Mathematical Modelling and Algorithms, vol. 3, no. 3, pp. 285–308, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. R. Musa, J.-P. Arnaout, and H. Jung, “Ant colony optimization algorithm to solve for the transportation problem of cross-docking network,” Computers and Industrial Engineering, vol. 59, no. 1, pp. 85–92, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. G. N. Varela and M. C. Sinclair, “Ant colony optimisation for virtual-wavelength-path routing and wavelength allocation,” in Proceedings of the 1999 Congress on Evolutionary Computation (CEC '99), pp. 1809–1816, Washington, DC, USA, July 1999. View at Publisher · View at Google Scholar · View at Scopus
  23. K. M. Sim and W. H. Sun, “Ant colony optimization for routing and load-balancing: survey and new directions,” Systems Man & Cybernetics Part A Systems Humans IEEE Transactions on, vol. 33, no. 5, pp. 560–572, 2003. View at Publisher · View at Google Scholar
  24. S.-H. Ngo, X. Jiang, and S. Horiguchi, “Adaptive routing and wavelength assignment using ant-based algorithm,” in Proceedings of the 2004 12th IEEE International Conference on Networks, ICON 2004 - Unity in Diversity, pp. 482–486, November 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. S. I. Vrieze, “Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC),” Psychological Methods, vol. 17, no. 2, pp. 228–243, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. D.-S. Huang and J.-X. Du, “A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks,” IEEE Transactions on Neural Networks, vol. 19, no. 12, pp. 2099–2115, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. B. V. North, D. Curtis, and P. C. Sham, “Application of logistic regression to case-control association studies involving two causative loci,” Human Heredity, vol. 59, no. 2, pp. 79–87, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. P.-J. Jing and H.-B. Shen, “MACOED: A multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies,” Bioinformatics, vol. 31, no. 5, pp. 634–641, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. N. Ryman, “CHIFISH: A computer program testing for genetic heterogeneity at multiple loci using chi-square and Fisher's exact test,” Molecular Ecology Notes, vol. 6, no. 1, pp. 285–287, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. C. R. Mehta and N. R. Patel, “A network algorithm for performing Fisher's exact test in r × c contingency tables,” Journal of the American Statistical Association, vol. 78, no. 382, pp. 427–434, 1983. View at Publisher · View at Google Scholar · View at MathSciNet
  31. B. Sobrino, M. Brión, and A. Carracedo, “SNPs in forensic genetics: A review on SNP typing methodologies,” Forensic Science International, vol. 154, no. 2-3, pp. 181–194, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. O. Shoval, H. Sheftel, G. Shinar et al., “Evolutionary trade-offs, pareto optimality, and the geometry of phenotype space,” Science, vol. 336, no. 6085, pp. 1157–1160, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. D.-S. Huang and W. Jiang, “A general CPL-AdS methodology for fixing dynamic parameters in dual environments,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 5, pp. 1489–1500, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. L. Zhu, W.-L. Guo, S.-P. Deng, and D.-S. Huang, “ChIP-PIT: enhancing the analysis of chip-seq data using convex-relaxed pair-wise interaction tensor decomposition,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 1, pp. 55–63, 2016. View at Publisher · View at Google Scholar · View at Scopus
  35. C. Angione, G. Carapezza, J. Costanza, P. Lio, and G. Nicosia, “Pareto optimality in organelle energy metabolism analysis,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 4, pp. 1032–1044, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. R. A. Fisher, “On the Interpretation of χ2 from Contingency Tables, and the Calculation of P,” Journal of the Royal Statistical Society, vol. 85, no. 1, p. 87, 1922. View at Publisher · View at Google Scholar
  37. A. Agresti, “A survey of exact inference for contingency tables,” Statistical Science, vol. 7, no. 1, pp. 131–153, 1992. View at Publisher · View at Google Scholar · View at Scopus
  38. B. Wenzheng, C. Yuehui, and W. Dong, “Prediction of protein structure classes with flexible neural tree,” Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3797–3806, 2014. View at Publisher · View at Google Scholar · View at Scopus
  39. L. Zhu, Z.-H. You, D.-S. Huang, and B. Wang, “t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks,” PLoS ONE, vol. 8, no. 4, Article ID e58368, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. C.-H. Zheng, L. Zhang, V. T.-Y. Ng, C. K. Shiu, and D.-S. Huang, “Molecular pattern discovery based on penalized matrix decomposition,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1592–1603, 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. D.-S. Huang and H.-J. Yu, “Normalized feature vectors: a novel alignment-free sequence comparison method based on the numbers of adjacent amino acids,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 457–467, 2013. View at Publisher · View at Google Scholar · View at Scopus
  42. J. Marchini, P. Donnelly, and L. R. Cardon, “Genome-wide strategies for detecting multiple loci that influence complex diseases,” Nature Genetics, vol. 37, no. 4, pp. 413–417, 2005. View at Publisher · View at Google Scholar · View at Scopus
  43. R. Jiang, W. Tang, X. Wu, and W. Fu, “A random forest approach to the detection of epistatic interactions in case-control studies,” BMC Bioinformatics, vol. 10, no. 1, article S65, 2009. View at Publisher · View at Google Scholar · View at Scopus
  44. J. Kruppa, A. Ziegler, and I. R. König, “Risk estimation and risk prediction using machine-learning methods,” Human Genetics, vol. 131, no. 10, pp. 1639–1654, 2012. View at Publisher · View at Google Scholar · View at Scopus
  45. D.-S. Huang and C.-H. Zheng, “Independent component analysis-based penalized discriminant method for tumor classification using gene expression data,” Bioinformatics, vol. 22, no. 15, pp. 1855–1862, 2006. View at Publisher · View at Google Scholar · View at Scopus
  46. R. W. Mahley, K. H. Weisgraber, and Y. Huang, “Apolipoprotein E4: a causative factor and therapeutic target in neuropathology, including Alzheimer's disease,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 15, pp. 5644–5651, 2006. View at Publisher · View at Google Scholar · View at Scopus
  47. E. M. Reiman, J. A. Webster, A. J. Myers et al., “GAB2 alleles modify Alzheimer's Risk in APOE ε4 carriers,” Neuron, vol. 54, no. 5, pp. 713–720, 2007. View at Publisher · View at Google Scholar · View at Scopus
  48. C.-H. Zheng, D.-S. Huang, L. Zhang, and X.-Z. Kong, “Tumor clustering using nonnegative matrix factorization with gene selection,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4, pp. 599–607, 2009. View at Publisher · View at Google Scholar · View at Scopus
  49. S.-P. Deng, L. Zhu, and D.-S. Huang, “Predicting hub genes associated with cervical cancer through gene co-expression networks,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 1, pp. 27–35, 2016. View at Publisher · View at Google Scholar · View at Scopus
  50. L. Zhu, S.-P. Deng, and D.-S. Huang, “A two-stage geometric method for pruning unreliable links in protein-protein networks,” IEEE Transactions on Nanobioscience, vol. 14, no. 5, pp. 528–534, 2015. View at Publisher · View at Google Scholar · View at Scopus
  51. D.-S. Huang, L. Zhang, K. Han, S. Deng, K. Yang, and H. Zhang, “Prediction of protein-protein interactions based on protein-protein correlation using least squares regression,” Current Protein and Peptide Science, vol. 15, no. 6, pp. 553–560, 2014. View at Publisher · View at Google Scholar · View at Scopus
  52. D.-S. Huang, Systematic Theory of Neural Networks for Pat-tern Recognition, Publishing House of Electronic Industry of China, May 1996.
  53. D.-S. Huang, “Radial basis probabilistic neural networks: model and application,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 13, no. 7, pp. 1083–1101, 1999. View at Publisher · View at Google Scholar · View at Scopus