Abstract
An important goal of human genetics is the identification of DNA sequence variations that are predictive of who is at risk for various common diseases. The focus of the present study is on the challenge of detecting and characterizing nonlinear attribute interactions or dependencies in the context of a genome-wide genetic study. The first question we address is whether the ReliefF algorithm is suitable for attribute selection in this domain. The second question we address is whether we can improve ReliefF for selecting important genetic attributes. Using simulated genetic datasets, we show that ReliefF is significantly better than a naïve chi-square test of independence for selecting two interacting attributes out of 103 candidates. In addition, we show that ReliefF can be improved in this domain by systematically removing the worst attributes and re-estimating ReliefF weights. Our simulation studies demonstrate that this new Tuned ReliefF (TuRF) algorithm is significantly better than ReliefF.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hirschhorn, J.N., Daly, M.J.: Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics 6, 95–108 (2005)
Wang, W.Y., Barratt, B.J., Clayton, D.G., Todd, J.A.: Genome-wide association studies: Theoretical and practical concerns. Nature Reviews Genetics 6, 109–118 (2005)
Bateson, W.: Mendel’s Principles of Heredity. Cambridge University Press, Cambridge (1909)
Moore, J.H.: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Human Heredity 56, 73–82 (2003)
Moore, J.H., Williams, S.W.: Traversing the conceptual divide between biological and statistical epistasis: Systems biology and a more modern synthesis. BioEssays 27, 637–646 (2005)
Thornton-Wells, T.A., Moore, J.H., Haines, J.L.: Genetics, statistics and human disease: Analytical retooling for complexity. Trends in Genetics 20, 640–647 (2004)
Freitas, A.: Understanding the crucial role of attribute interactions. Artificial Intelligence Review 16, 177–199 (2001)
Li, W., Reich, J.: A complete enumeration and classification of two-locus disease models. Human Heredity 50, 334–349 (2000)
Moore, J.H., Ritchie, M.D.: The challenges of whole-genome approaches to common diseases. JAMA 291, 1642–1643 (2004)
Moore, J.H., Williams, S.W.: New strategies for identifying gene-gene interactions in hypertension. Annals of Medicine 34, 88–95 (2002)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Machine Learning: Proceedings of the AAAI’92 (1992)
Kononenko, I.: Estimating attributes: Analysis and extension of relief. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)
Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and rrelieff. Machine Learning 53, 23–69 (2003)
Robnik-Sikonja, M., Kononenko, I.: Comprehensible interpretation of relief’s estimates. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 433–440 (2001)
Hahn, L.W., Moore, J.H.: Ideal discrimination of discrete clinical endpoints using multilocus genotypes. Silico Biology 4, 183–194 (2004)
Hahn, L.W., Ritchie, M.D., Moore, J.H.: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19, 376–382 (2003)
Moore, J.H.: Computational analysis of gene-gene interactions in common human diseases using multifactor dimensionality reduction. Expert Review of Molecular Diagnostics 4, 795–803 (2004)
Moore, J.H., Gilbert, J.C., Tsai, C., Chiang, F.T., Holden, W., Barney, N., White, B.C.: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Journal of Theoretical Biology 241, 252–261 (2006)
Ritchie, M.D., Hahn, L.W., Moore, J.H.: Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, phenocopy, and genetic heterogeneity. Genetic Epidemiology 24, 150–157 (2003)
Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. American Journal of Human Genetics 69, 138–147 (2001)
Moore, J.H., White, B.C.: Exploiting expert knowledge in genetic programming for genome-wide genetic analysis. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 969–977. Springer, Heidelberg (2006)
Moore, J.H.: Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. In: Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data. IGI (2007)
Moore, J.H., White, B.C.: Genome-wide genetic analysis using genetic programming: The critical need for expert knowledge. In: Genetic Programming Theory and Practice, vol. 4, Springer, Heidelberg (2007)
Lui, H., Motoda, H., Yu, L.: A selective sampling approach to active feature selection. Artificial Intelligence 159, 49–74 (2004)
Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, L.: Data mining in bioinformatics using weka. Bioinformatics 20, 2479–2481 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Moore, J.H., White, B.C. (2007). Tuning ReliefF for Genome-Wide Genetic Analysis. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-71783-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71782-9
Online ISBN: 978-3-540-71783-6
eBook Packages: Computer ScienceComputer Science (R0)