Abstract
Detecting genetic interactions without running an exhaustive search is a difficult problem. We present a new heuristic, multiSURF*, which can detect these interactions with high accuracy and in time linear in the number of genes. Our algorithm is an improvement over the SURF* algorithm, which detects genetic signals by comparing individuals close to, and far from, one another and noticing whether differences correlate with different disease statuses. Our improvement consistently outperforms SURF* while providing a large runtime decrease by examining only individuals very near and very far from one another. Additionally we perform an analysis on real data and show that our method provides new information. We conclude that multiSURF* is a better alternative to SURF* in both power and runtime.
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
Cordell, J.H.: Detecting genegene interactions that underlie human diseases. Nature Reviews Genetics 489, 392–404 (2009)
Greene, C.S., Himmelstein, D.S., Kiralis, J., Moore, J.H.: The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 182–193. Springer, Heidelberg (2010)
Greene, C.S., Penrod, N.M., Kiralis, J., Moore, J.H.: Spatially uniform relieff (surf) for computationally-efficient filtering of gene-gene interactions. BioData Mining 2 (2009)
Kira, K., Rendell, L.A.: A practical approach to feature selection. Machine Learning, 249–256 (1992)
Kononenko, I.: Estimating Attributes: Analysis and Extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)
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 (2007)
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.M.: Epistasis and its implications for personal genetics. AJHG 85, 309–320 (2009)
Sokal, R.R., Rohlf, F.J.: Biometry: the principles and practice of statistics in biological research, 3rd edn.
Thomas, D.: Gene-environment-wide association studies. Nat. Rev. Genetics 11, 259–272 (2010)
Velez, D.R., White, B.C., Motsinger, A.A., Bush, W.S., Ritchie, M.D., Williams, S.M., Moore, J.H.: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genetic Epidemiology 31, 306–315 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Granizo-Mackenzie, D., Moore, J.H. (2013). Multiple Threshold Spatially Uniform ReliefF for the Genetic Analysis of Complex Human Diseases. In: Vanneschi, L., Bush, W.S., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science, vol 7833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37189-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-37189-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37188-2
Online ISBN: 978-3-642-37189-9
eBook Packages: Computer ScienceComputer Science (R0)