We investigate the problem of identification of genes correlated with the occurrence of diseases in a given population. The classical method of parametric linkage analysis is combined with newer tools and results are achieved on a model problem. This traditional method has advantages over non-parametric methods, but these advantages have been difficult to realize due to their high computational cost. We study a class of Evolutionary Algorithms from the Computational Intelligence literature which are designed to cut such costs considerably for optimization problems. We outline the details of this algorithm, called Particle Swarm Optimization, and present all the equations and parameter values we used to accomplish our optimization. We view this study as a launching point for a wider investigation into the leveraging of computational intelligence tools in the study of complex biological systems.
J. E. Seiffertt et al., "Maximum Likelihood Methods in Biology Revisited with Tools of Computational Intelligence," Proceedings of the 30th Annual International IEEE EMBS Conference (2008, Vancouver, British Columbia, Canada), Institute of Electrical and Electronics Engineers (IEEE), Aug 2008.
The definitive version is available at https://doi.org/10.1109/IEMBS.2008.4649683
30th Annual International IEEE EMBS Conference (2008: Aug. 20-24, Vancouver, British Columbia, Canada)
Electrical and Computer Engineering
Keywords and Phrases
Article - Conference proceedings
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