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Articles

A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework

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Pages 511-523 | Received 28 Mar 2014, Accepted 25 Nov 2014, Published online: 23 Feb 2015

References

  • W. Bateson, Heredity and variation in modern lights, Darwin Modern Sci. (1909), pp. 85–101.
  • M.S. Bergholt, W. Zheng, K. Lin, K.Y. Ho, M. Teh, K.G. Yeoh, J.B. Yan So, and Z. Huang, In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony optimization techniques, Int. J. Cancer 128 (2011), pp. 2673–2680. doi: 10.1002/ijc.25618
  • J. Christmas, E. Keedwell, T.M. Frayling, and J.R.B. Perry, Ant colony optimisation to identify genetic variant association with type 2 diabetes, Inf. Sci. 181 (2011), pp. 1609–1622. doi: 10.1016/j.ins.2010.12.005
  • M. Dorigo and M. Birattari, Ant Colony Optimization, Encyclopedia of Machine Learning, Springer, 2010, 36–39.
  • C.S. Greene, N.M. Penrod, J. Kiralis, and J.H. Moore, Spatially uniform relieff (surf) for computationally-efficient filtering of gene–gene interactions, BioData Mining 2 (2009), pp. 1–9. doi: 10.1186/1756-0381-2-5
  • M.-H. Hall, D.L. Levy, D.F. Salisbury, S. Haddad, P. Gallagher, M. Lohan, B. Cohen, D. Ongur, and J.W. Smoller, Neurophysiologic effect of GWAS derived schizophrenia and bipolar risk variants, Am. J. Med. Genet. Part B Neuropsychiat. Genet. 165 (2014), pp. 9–18. doi: 10.1002/ajmg.b.32212
  • P. Jia and Z. Zhao, Network-assisted analysis to prioritize GWAS results: Principles, methods and perspectives, Human Genet. 133 (2014), pp. 125–138. doi: 10.1007/s00439-013-1377-1
  • Y. Kirino, G. Bertsias, Y. Ishigatsubo, N. Mizuki, I. Tugal-Tutkun, E. Seyahi, Y. Ozyazgan, F.S. Sacli, B. Erer, H. Inoko, Z. Emrence, A. Cakar, N. Abaci, D. Ustek, C. Satorius, A. Ueda, M. Takeno, Y. Kim, G.M Wood, M.J. Ombrello, A. Meguro, A. Gül, E.F Remmers, and D.L Kastner, Genome-wide association analysis identifies new susceptibility loci for Behcet's disease and epistasis between HLA-B[ast]51 and ERAP1, Nat. Genet. 45 (2013), pp. 202–207. doi: 10.1038/ng.2520
  • I.B. McInnes and G. Schett, The pathogenesis of rheumatoid arthritis, New Engl. J. Med. 365 (2011), pp. 2205–2219. doi: 10.1056/NEJMra1004965
  • A.A. Motsinger-Reif, S.M. Dudek, L.W. Hahn, and M.D. Ritchie, Comparison of approaches for machine-learning optimization of neural networks for detecting gene–gene interactions in genetic epidemiology, Genet. Epidemiol. 32 (2008), pp. 325–340. doi: 10.1002/gepi.20307
  • J.R. Quevedo, A. Bahamonde, M. Pérez-Enciso, and O. Luaces, Disease liability prediction from large scale genotyping data using classifiers with a reject option, IEEE/ACM Trans. Comput. Biol. Bioinform. 9 (2012), pp. 88–97. doi: 10.1109/TCBB.2011.44
  • M.D. Ritchie, L.W. Hahn, N. Roodi, L.R. Bailey, W.D. Dupont, F.F. Parl, and J.H. Moore, Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer, Am. J. Human Genet. 69 (2001), pp. 138–147. doi: 10.1086/321276
  • M.D. Ritchie, B.C. White, J.S. Parker, L.W. Hahn, and J.H. Moore, Optimization of neural network architecture using genetic programming improves detection and modeling of gene–gene interactions in studies of human diseases, BMC Bioinform. 4 (2003), p. 28. doi: 10.1186/1471-2105-4-28
  • J. Shang, J. Zhang, Y. Sun, D. Liu, D. Ye, and Y. Yin, Performance analysis of novel methods for detecting epistasis, BMC Bioinform. 12 (2011), p. 475. doi: 10.1186/1471-2105-12-475
  • J. Shang, J. Zhang, X. Lei, Y. Zhang, and B. Chen, Incorporating heuristic information into ant colony optimization for epistasis detection, Genes Genom. 34 (2012), pp. 321–327. doi: 10.1007/s13258-012-0003-2
  • W. Shi, K.E. Lee, and G. Wahba, Detecting disease-causing genes by LASSO-Patternsearch algorithm, BMC Proceedings, Vol. 1, London and New York, 2007, p. S60.
  • A. Sulovari, J. Kiralis, and J.H. Moore, Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease, Springer, Berlin, 2013.
  • L. Tiret, P. Ducimetière, A. Bonnardeaux, F. Soubrier, O. Poirier, S. Ricard, F. Cambien, P. Marques-Vidal, A. Evans, F. Kee, D. Arveiler, and G. Luc, Synergistic effects of angiotensin-converting enzyme and angiotensin-II type 1 receptor gene polymorphisms on risk of myocardial infarction, Lancet 344 (1994), pp. 910–913. doi: 10.1016/S0140-6736(94)92268-3
  • C.T. Tsai, L.P. Lai, J.L. Lin, F.T. Chiang, J.J. Hwang, M.D. Ritchie, J.H. Moore, K.L. Hsu, C.D. Tseng, C.S. Liau, and Y.-Z. Tseng, Renin–angiotensin system gene polymorphisms and atrial fibrillation, Circulation 109 (2004), pp. 1640–1646. doi: 10.1161/01.CIR.0000124487.36586.26
  • D.R. Velez, B.C. White, A.A. Motsinger, W.S. Bush, M.D. Ritchie, S.M. Williams, and J.H. Moore, A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction, Genet. Epidemiol. 31 (2007), pp. 306–315. doi: 10.1002/gepi.20211
  • Y. Wang, X. Liu, and R. Rekaya, AntEpiSeeker2.0: Extending epistasis detection to epistasis-associated pathway inference using ant colony optimization, Nature Publishing Group, London, 2012.
  • 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 Res. Notes 3 (2010), p. 117. doi: 10.1186/1756-0500-3-117
  • C. Yang, Z. He, X. Wan, Q. Yang, H. Xue, and W. Yu, SNPHarvester: A filtering-based approach for detecting epistatic interactions in genome-wide association studies, Bioinformatics 25 (2009), pp. 504–511. doi: 10.1093/bioinformatics/btn652
  • X. Zhang, F. Zou, and W. Wang, FastANOVA: An efficient algorithm for genome-wide association study, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London and New York, 2008, pp. 821–829.
  • X. Zhang, F. Zou, and W. Wang, FastChi: An efficient algorithm for analyzing gene–gene interactions, Pacific Symposium on Biocomputing, 2009, p. 528.
  • X. Zhang, S. Huang, F. Zou, and W. Wang, Team: Efficient two-locus epistasis tests in human genome-wide association study, Bioinformatics 26 (2010), pp. i217–i227. doi: 10.1093/bioinformatics/btq186
  • Y. Zhang and J.S. Liu, Bayesian inference of epistatic interactions in case-control studies, Nat. Genet. 39 (2007), pp. 1167–1173. doi: 10.1038/ng2110
  • Z. Zhou, G. Liu, L. Su, L. Yan, and L. Han, CChi: An efficient cloud epistasis test model in human genome wide association studies, 2013 Sixth International Conference on Biomedical Engineering and Informatics (BMEI), Hangzhou, Zhejiang Province, China, 2013, pp. 787–791.

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