ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Boosting Multifactor Dimensionality Reduction Using Pre-evaluation

  • Hong, Yingfu (Department of Nanobiomedical Science, Dankook University) ;
  • Lee, Sangbum (Department of Computer Science, Dankook University) ;
  • Oh, Sejong (Department of Nanobiomedical Science, Dankook University)
  • Received : 2014.07.26
  • Accepted : 2015.07.24
  • Published : 2016.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

The detection of gene-gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the "curse of dimensionality" -that is, it works well for two- or three-way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10-way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre-evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.

Keywords

References

  1. A.A. Motsinger and M.D. Ritchie, "Multifactor Dimensionality Reduction: An Analysis Strategy for Modeling and Detecting Gene-Gene Interactions in Human Genetics and Pharmacogenomics Studies," Human Genomics, vol. 2, no. 5, Mar. 2006, pp. 318-328.
  2. H.J. Cordell, "Detecting Gene-Gene Interactions that Underlie Human Diseases," Nature Rev. Genetics, vol. 10, no. 6, June 2009, pp. 392-404. https://doi.org/10.1038/nrg2579
  3. M.D. Ritchie et al., "Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer," American J. Human Genetics, vol. 69, no. 1, July 2001, pp. 138-147. https://doi.org/10.1086/321276
  4. M.R. Nelson et al., "A Combinatorial Partitioning Method to Identify Multilocus Genotypic Partitions that Predict Quantitative Trait Variation," Genome Res., vol. 11, no. 3, Mar. 2001, pp. 458-470. https://doi.org/10.1101/gr.172901
  5. C.-F. Li et al., "Weighted Risk Score-Based Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions in Nasopharyngeal Carcinoma," Int. J. Molecular Sci., vol. 15, no. 6, 2014, pp. 10724-10737. https://doi.org/10.3390/ijms150610724
  6. A.V. Bulinski, "On Foundation of the Dimensionality Reduction Method for Explanatory Variables," J. Math. Sci., vol. 199, no. 2, May 2014, pp. 113-122. https://doi.org/10.1007/s10958-014-1838-7
  7. X.-Y. Lou et al., "A Generalized Combinatorial Approach for Detecting Gene-by-Gene and Gene-by-Environment Interactions with Application to Nicotine Dependence," American J. Human Genetics, vol. 80, no. 6, June 2007, pp. 1125-1137. https://doi.org/10.1086/518312
  8. W.S. Bush et al., "Alternative Contingency Table Measures Improve the Power and Detection of Multifactor Dimensionality Reduction," BMC Bioinformatics, vol. 9, May 2008, p. 238. https://doi.org/10.1186/1471-2105-9-238
  9. H.-M. Xu et al., "Multivariate Dimensionality Reduction Approaches to Identify Gene-Gene and Gene-Environment Interactions Underlying Multiple Complex Traits," PLoS One, vol. 9, no. 9, Sept. 2014, pp. 10724-10737.
  10. G.-B. Chen et al., "A Unified GMDR Method for Detecting Gene-Gene Interactions in Family and Unrelated Samples with Application to Nicotine Dependence," Human Genetics, vol. 133, no. 2, Feb. 2014, pp. 139-150. https://doi.org/10.1007/s00439-013-1361-9
  11. J.M. Fisher et al., "Cell-Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction," in Evol. Comput., Mach. Learning Data Mining Bioinformatics, New York, USA: Springer, vol. 7833, 2013, pp. 200-211.
  12. S. Lee et al., "A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype," Biomed Res. Int., vol. 2015, 2015, pp. 1-7.
  13. N.A. Sinnott-Armstrong, C.S. Greene, and J.H. Moore, "Fast Genome-Wide Epistasis Analysis Using Ant Colony Optimization for Multifactor Dimensionality Reduction Analysis on Graphics Processing Units," Annual Conf. Genetic Evol. Comput., Portland, OR, USA, July 7-11, 2010, pp. 215-216.
  14. J. Gui et al., "A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits," PLoS One, vol. 8, no. 6, June 2013, pp. 1-7.
  15. P. Yang et al., "Gene-Gene Interaction Filtering with Ensemble of Filters," BMC Bioinformatics, vol. 12, no. 1, Feb. 2011, pp. 1-10. https://doi.org/10.1186/1471-2105-12-1
  16. V. Kumar and S. Minz "Feature Selection: A Literature Review," Smart Comput. Rev., vol. 4, no. 3, June 2014, pp. 211-229.
  17. E.E. Ghiselli, "Theory of Psychological Measurement," New York, USA: McGraw-Hill, 1964, pp. 11-19.
  18. M.A. Hall, Correlation-Based Feature Selection for Machine Learning, Ph.D. dissertation, Univ. of Waikato, Waikato, New Zealand, 1999.
  19. S. Amari and S. Wu, "Improving Support Vector Machine Classifiers by Modifying Kernel Functions," Neural Netw., vol. 12, no. 6, July 1999, pp. 783-789. https://doi.org/10.1016/S0893-6080(99)00032-5
  20. M.A. Province and I.B. Borecki, "Gathering the Gold Dust: Methods for Assessing the Aggregate Impact of Small Effect Genes in Genomic Scans," Pacific Sym. Biocomp., Kohala Coast, HI, USA, vol. 13, Jan. 4-8, 2008, pp. 190-200.
  21. Y. Zhang and J.S. Liu, "Bayesian Inference of Epistatic Interactions in Case-Control Studies," Nature Genetics, vol. 39, no. 9, Aug. 2007, pp. 1167-1173. https://doi.org/10.1038/ng2110