Abstract
With the first draft completion of multiple organism genome sequencing programmes the emphasis is now moving toward a functional understanding of these genes and their network interactions. Microarray technology allows for large-scale gene experimentation. Using this technology it is possible to find the expression levels of genes across different conditions. The use of a genetic algorithm with a backpropagation local searching mechanism to reconstruct gene networks was investigated. This study demonstrates that the distributed genetic algorithm approach shows promise in that the method can infer gene networks that fit test data closely. Evaluating the biological accuracy of predicted networks from currently available test data is not possible. The best that can be achieved is to produce a set of possible networks to pass to a biologist for experimental verification.
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References
Shin Ando and Hitoshi Iba. Inference of gene regulatory model by genetic algorithms. Proceedings of the 2001 IEEE Congress on Evolutionary Computation. Seoul Korea, 2001.
C. Weaver et. al. Modeling regulatory networks with weight matrices, pacific symposium on biocomputing. Journal of Computational Biology, 4:112–123, 1999.
Carol A. et. al. Extraordinary bones functional and genetic analysis of the ext gene family. Current Genomics, 2:91–124, 2001.
Goldberg et. al. The design of innovation, lessons from genetic algorithms. Technological Forecasting and Social Change.In press, 2000.
Hughes et. al. Functional discovery via a compendium of expression profiles. Cell, 102:109–126, 2000.
Kim K et. al. Identification of gene sets from cdna microarrays using classification trees. Technical report, Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, USA, 2001.
Lawrence J. Lesko et. al. Pharmacogenomic-guided drug development regulatory perspective drug evaluation and research. Technical report, Center for Drug Evaluation and Research, 5600 Fishers Lane Rockville, Maryland 20852 USA, 2001.
Werbos P. J. Backpropagation through time, what it does and how to do it. Proceedings of the 1990 IEEE, 78:1550–1560, 1990.
J. Hertz A. Krogh and R.G. Palmer. Introduction to the Theory of Neural Computation. Addison-Wesley, 1991.
I. Nachman. Nir Firedman., with M. Linial. and D. Pe'er. Using bayesian networks to analyze expression data. Journal of Computational Biology, 7:601–620, 2000.
D. E. Rumelhart and J. L. McClelland. Explorations in Parallel Distributed Processing. MIT Press, 1988.
Richardson S. Mixture model for the identifying gene expression factors in survival analysis using microarray experiments. Technical report, Department of Epidemiology and Public Health, Imperial College, London, UK, 2001.
Darrell Whitley. A genetic algorithm tutorial. Technical report, Computer Science Department, Colorado State University, Colorado State University Fort Collins, CO 80523.
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Cumiskey, M., Levine, J., Armstrong, D. (2003). Gene Network Reconstruction Using a Distributed Genetic Algorithm with a Backprop Local Search. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_4
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DOI: https://doi.org/10.1007/3-540-36605-9_4
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