Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease
The fast measurement of millions of sequence variations across the genome is possible with the current technology. As a result, a difficult challenge arise in bioinformatics: the identification of combinations of interacting DNA sequence variations predictive of common disease . The Multifactor Dimensionality Reduction (MDR) method is capable of analysing such interactions but an exhaustive MDR search would require exponential time. Thus, we use the Ant Colony Optimization (ACO) as a stochastic wrapper. It has been shown by Greene et al. that this approach, if expert knowledge is incorporated, is effective for analysing large amounts of genetic variation. In the ACO method integrated in the MDR package, a linear and an exponential probability distribution function can be used to weigh the expert knowledge. We generate our biological expert knowledge from a network of gene-gene interactions produced by a literature mining platform, Pathway Studio. We show that the linear distribution function of expert knowledge is the most appropriate to weigh our scores when expert knowledge from literature mining is used. We find that ACO parameters significantly affect the power of the method and we suggest values for these parameters that can be used to optimize MDR in Genome Wide Association Studies that use biological expert knowledge.
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- 1.Greene, C.S., Gilmore, J.M., Kiralis, J., Andrews, P.C., Moore, J.H.: Optimal Use of Expert Knowledge in Ant Colony Optimization for the Analysis of Epistasis in Human Disease. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 92–103. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 3.The International HapMap Consortium: A second Generation human haplotype of over 3.1 million SNPs. Nature 449, 851–861 (2007)Google Scholar
- 5.Moore, J.H., Gilbert, J.C., Tsai, C.T., Chiang, F.T., Holden, T., 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(2), 252–261 (2006)MathSciNetCrossRefGoogle Scholar
- 12.Urbanowicz, R.J., Kiralis, J., Sinnot-Armstrong, N.A., Heberling, T., Fisher, J.M., Moore, J.H.: GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Mining 5(16) (2012)Google Scholar
- 16.Sokal, R.R., Rohlf, F.J.: Biometry: the principles and practice of statistics in biological research, 3rd edn. W.H. Freeman and Co., New York (1995)Google Scholar
- 17.Dorigo, M., Maniezzo, V., Colorni, A.: Positive Feedback as a search strategy. Dipartimento di Elettronica e Informatica, Politecnico di Milano, Technical Reports, 91–116 (1991)Google Scholar
- 18.Dorigo, M., Stützle, T.: Ant Colony Optimization (2004)Google Scholar
- 20.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 IV. Springer (2007)Google Scholar