Abstract
The complex dynamics of Human Immunodeficiency Virus leads to serious problems on predicting the drug resistance. Several machine learning techniques have been proposed for modelling this classification problem, but most of them are difficult to aggregate and interpret. In fact, in last years the protein modelling of this virus has become, from diverse points of view, an open problem for researchers. This paper presents a modelling of the protease protein as a dynamic system through Fuzzy Cognitive Maps, using the amino acids contact energies for the sequence description. In addition, a learning scheme based on swarm intelligence called PSO-RSVN is used to estimate the causal weight matrix that characterizes these structures. Finally, an aggregation procedure with previously adjusted maps is applied for obtaining a prototype map, in order to discover knowledge in the causal influences, and simulate the system behaviour when a single (or multiple) mutation takes place.
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References
Prosperi, M., Ulivi, G.: Evolutionary Fuzzy Modelling for Drug Resistant HIV-1 Treatment Optimization. SCI, vol. 82, pp. 251–287. Springer, Heidelberg (2008)
Beerenwinkel, N., et al.: Computational methods for the design of effective therapies against drug resistant HIV strains. Bioinformatics 21, 3943–3950 (2005)
Drǎghici, S., Potter, R.: Predicting HIV drug resistance with neural networks. Bioinformatics 19, 98–107 (2003)
Bonet, I., García, M.M., Saeys, Y., Van de Peer, Y., Grau, R.: Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4527, pp. 234–243. Springer, Heidelberg (2007)
Beerenwinkel, N., et al.: Geno2pheno: interpreting genotypic HIV drug resistance tests. IEEE Intelligence System 16, 35–41 (2001)
Beerenwinkel, N., et al.: Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype. Proceedings of the National Academy of USA 99, 8271–8276 (2002)
Kosko, B.: Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24, 65–75 (1986)
Kosko, B.: Neural Networks and Fuzzy systems, a dynamic system approach to machine intelligence. Prentice-Hall, Englewood Cliffs (1992)
Castillo, E.: Expert Systems and Probabilistic Network Models. Springer (2003)
León, M., Nápoles, G., Rodriguez, C., García, M.M., Bello, R., Vanhoof, K.: A Fuzzy Cognitive Maps Modeling, Learning and Simulation Framework for Studying Complex System. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part II. LNCS, vol. 6687, pp. 243–256. Springer, Heidelberg (2011)
Miyazawa, S., Jernigan, R.L.: Contacts energies Self-Consistent Estimation of Inter-Residue Protein Contact Energies Based on an Equilibrium Mixture Approximation of Residues. PROTEINS: Structure, Function, and Genetics 34, 49–68 (1999)
McMichael, J.M., et al.: Optimizing Fuzzy Cognitive Maps with a Genetic Algorithm. In: AIAA 1st Intelligent Systems Technical Conference, Chicago, Illinois (2004)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Australia, pp. 1942–1948 (1995)
Bratton, D., Kennedy, J.: Defining a Standard for Particle Swarm Optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007), Honolulu, HI, pp. 120–127 (2007)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Kennedy, J., Russell, C.E.: Swarm Intelligence. Morgan Kaufmann Publishers (2001)
Nápoles, G., Grau, I., Bello, R.: Particle Swarm Optimization with Random Sampling in Variable Neighbourhoods for Solving Global Minimization Problems. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 352–353. Springer, Heidelberg (2012)
León, M., Nápoles, G., García, M.M., Bello, R., Vanhoof, K.: Two Steps Individuals Travel Behavior Modeling through Fuzzy Cognitive Maps Pre-definition and Learning. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part II. LNCS, vol. 7095, pp. 82–94. Springer, Heidelberg (2011)
Stanford HIV Drug Resistance Database, http://hivdb.stanford.edu
Woods, M., Carpenter, G.A.: Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance. CAS/CNS Technical Report 2007-004 (2007)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers (2011)
Bonet, I.: Modelo para la clasificación de secuencias, en problemas de la bioinformática, usando técnicas de inteligencia artificial. PhD Thesis (2008)
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Nápoles, G., Grau, I., León, M., Grau, R. (2013). Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_17
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DOI: https://doi.org/10.1007/978-3-642-37798-3_17
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