A Decision Support System Based on Multi-Agent Technology for Gene Expression Analysis
Author(s)
Edna Márquez1*,
Jesús Savage1,
Jaime Berumen2,
Christian Lemaitre3,
Ana Lilia Laureano-Cruces4,
Ana Espinosa2,
Ron Leder1,
Alfredo Weitzenfeld5
Affiliation(s)
1 Facultad de Ingeniería, Universidad Nacional Autónoma de México, México D.F., México. 2 Unidad de Medicina Genómica, Hospital General de México, México D.F., México. 3 Departamento de Ciencias de la Comunicación, Universidad Autónoma Metropolitana, México D.F., México. 4 Departamento de Sistemas, Universidad Autónoma Metropolitana, México D.F., México. 5 Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
1 Facultad de Ingeniería, Universidad Nacional Autónoma de México, México D.F., México. 2 Unidad de Medicina Genómica, Hospital General de México, México D.F., México. 3 Departamento de Ciencias de la Comunicación, Universidad Autónoma Metropolitana, México D.F., México. 4 Departamento de Sistemas, Universidad Autónoma Metropolitana, México D.F., México. 5 Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
ABSTRACT
The genetic microarrays give to researchers a huge amount of data of many diseases represented by intensities of gene expression. In genomic medicine gene expression analysis is guided to find strategies for prevention and treatment of diseases with high rate of mortality like the different cancers. So, genomic medicine requires the use of complex information technology. The purpose of our paper is to present a multi-agent system developed in order to improve gene expression analysis with the automation of tasks about identification of genes involved in a cancer, and classification of tumors according to molecular biology. Agents that integrate the system, carry out reading files of intensity data of genes from microarrays, pre-processing of this information, and with machine learning methods make groups of genes involved in the process of a disease as well as the classification of samples that could propose new subtypes of tumors difficult to identify based on their morphology. Our results we prove that the multi-agent system requires a minimal intervention of user, and the agents generate knowledge that reduce the time and complexity of the work of prevention and diagnosis, and thus allow a more effective treatment of tumors.
The genetic microarrays give to researchers a huge amount of data of many diseases represented by intensities of gene expression. In genomic medicine gene expression analysis is guided to find strategies for prevention and treatment of diseases with high rate of mortality like the different cancers. So, genomic medicine requires the use of complex information technology. The purpose of our paper is to present a multi-agent system developed in order to improve gene expression analysis with the automation of tasks about identification of genes involved in a cancer, and classification of tumors according to molecular biology. Agents that integrate the system, carry out reading files of intensity data of genes from microarrays, pre-processing of this information, and with machine learning methods make groups of genes involved in the process of a disease as well as the classification of samples that could propose new subtypes of tumors difficult to identify based on their morphology. Our results we prove that the multi-agent system requires a minimal intervention of user, and the agents generate knowledge that reduce the time and complexity of the work of prevention and diagnosis, and thus allow a more effective treatment of tumors.
Cite this paper
Márquez, E. , Savage, J. , Berumen, J. , Lemaitre, C. , Laureano-Cruces, A. , Espinosa, A. , Leder, R. and Weitzenfeld, A. (2015) A Decision Support System Based on Multi-Agent Technology for Gene Expression Analysis. International Journal of Intelligence Science, 5, 158-172. doi: 10.4236/ijis.2015.53014.
Márquez, E. , Savage, J. , Berumen, J. , Lemaitre, C. , Laureano-Cruces, A. , Espinosa, A. , Leder, R. and Weitzenfeld, A. (2015) A Decision Support System Based on Multi-Agent Technology for Gene Expression Analysis. International Journal of Intelligence Science, 5, 158-172. doi: 10.4236/ijis.2015.53014.
References
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http://dx.doi.org/10.1007/978-3-540-45012-2_7 [5] Lam, H.C., Garcia, M.V., Juneja, B., Fahrenkrug, S. and Boley, D. (2006) Gene Expression Analysis in Multi-Agent Environment. International Transactions on Systems Science and Applications, 1. [6] Jin, L., Steiner, K., Schmidt, C. and Situ, G. (2005) A Multiagent Framework to Integrate and Visualize Gene Expression Information. IEEE ICDM Workshop on MADW & MADM, 1-7.
http://www.eecis.udel.edu/~kamboj/pubs/kamboj.madw.05.pdf [7] Stiglic, G. and Kokol, P. (2004) Using Multi-Agent System for Gene Expression Classification. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, 1-5 September 2004, 2952-2955. [8] Wooldridge, M. (2002) An Introduction to Multiagent Systems. John Wiley, USA. [9] Laureano-Cruces, A.L., Ramírez-González, T., Sánchez-Guerrero, L. and Ramírez-Rodríguez, J. (2014) Multi-Agent System for Real Time Planning Using Collaborative Agents. International Journal of Intelligence Science, 4, 91-103. [10] Laureano-Cruces, A.L. and Espinoza-Paredes, G. (2005) Behavioral Design to Model a Reactive Decision of an Expert in Geothermal Wells. International Journal of Approximate Reasoning, 39, 1-28.
http://dx.doi.org/10.1016/j.ijar.2004.08.002 [11] Jennings, N. (2001) An Agent-Based Approach for Building Complex Software Systems. Communications of the ACM, 44, 35-41.
http://dx.doi.org/10.1145/367211.367250 [12] Koutkias, V., Malousi, A. and Maglaveras, N. (2007) Engineering Agent-Mediated Integration of Bioinformatics Analysis Tools. Multiagent and Grid Systems, 3, 245-258. [13] Kohonen, T. (1995) Self Organizing Maps. Springer, Berlin. [14] Espinosa, A., Alfaro, A., Roman-Basaure, E., Guardado-Estrada, M., Palma, í., Serralde, C., et al. (2013) Mitosis Is a Source of Potential Markers for Screening and Survival and Therapeutic Targets in Cervical Cancer. PLoS ONE, 8, e55975.
http://dx.doi.org/10.1371/journal.pone.0055975 [15] Karasavvas, K., Burger, A. and Baldock, R. (2004) Bioinformatics Integration and Agent Technology. Journal of Biomedical Informatics, 37, 205-219. http://dx.doi.org/10.1016/j.jbi.2004.04.003 [16] Clips (2014) http://clipsrules.sourceforge.net/ [17] Gentleman, R., Carey, V., Huber, W., Irizarry, R. and Dudoit, S. (2005) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer.
http://www.bioconductor.org/ [18] Márquez, E., Savage, J., Espinosa, A., Berumen, J. and Lemaitre, C. (2008) Gene Expression Analysis for Tumor Classification Using Vector Quantization. 3rd IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008), Melbourne, 15-17 October 2008, 95-103. [19] National Center for Biotechnology Information (2014) http://www.ncbi.nlm.nih.gov/ [20] DAVID Bioinformatics Resources (2014) http://david.abcc.ncifcrf.gov/ [21] Luck, M. and d’Inverno, M. (2001) A Conceptual Framework for Agent Definition and Development. The Computer Journal, 44, 1-20. [22] Sánchez-Guerrero, L., Laureano-Cruces, A.L., Mora-Torres, M., Ramírez-Rodríguez, J. and Silva-López, R. (2013) A Multi-Agent Intelligent Learning System: An Application with a Pedagogical Agent and Learning Objects. Creative Education, 4, 181-190.
http://www.scirp.org/journal/ce [23] Laureano-Cruces, A.L., Acevedo-Moreno, D.A., Mora-Torres, M. and Ramirez-Rodriguez, J. (2012) A Reactive Behavior Agent: Including Emotions for a Video Game. Journal of Applied Research and Technology (CCADET-UNAM), 10, 651-672. [24] Sánchez-Guerrero, L., Laureano-Cruces, A.L., Mora-Torres, M. and Ramìrez-Rodríguez, J. (2011) Multiagent Architecture for Errors Management in Content Organized in Learning Objects. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2011, Hawai, 17-21 October 2011, 2462-2467. www.EdiTLib.org [25] Laureano-Cruces, A.L. and Verduga-Palencia, D.O. (2010) Simulación de un juego de futbol utilizando una arquitectura Multiagente-Reactiva. In Libro: Desarrollo Tecnológico. (Alfa-Omega), XXIII Congreso Nacional y XI Congreso Internacional de Informática y Computación de la ANIEI, Puerto Vallarta, 11-15 de octubre, 485-493. [26] Berman, J. (2004) Tumor Classification: Molecular Analysis Meets Aristotle. BMC Cancer, 4, 10.
[1] Tinker, A.V., Boussioutas, A. and Bowtell, D.D.L. (2006) The Challenges of Gene Expression Microarrays for the Study of Human Cancer. Cancer Cell, 9, 333-339. [2] Merelli, E. and Luck, M. (2005) Agents in Bioinformatic. Knowledge Engineering Review, 20, 117-125. [3] Merelli, E., Armano, G., Cannata, N., Corradini, F., d’Inverno, M., Doms, A., Lord, P., Martin, A., Milanesi, L., Moller, S., Schroeder, M. and Luck, M. (2006) Agents in Bioinformatics, Computational and Systems Biology. Briefings in Bioinformatics, 8, 45-59. [4] Bryson, K., Luck, M., Joy, M. and Jones, D. (2000) Applying Agents to Bioinformatics in GeneWeaver. Lecture Notes in Computer Science, 1860, 60-71.
http://dx.doi.org/10.1007/978-3-540-45012-2_7 [5] Lam, H.C., Garcia, M.V., Juneja, B., Fahrenkrug, S. and Boley, D. (2006) Gene Expression Analysis in Multi-Agent Environment. International Transactions on Systems Science and Applications, 1. [6] Jin, L., Steiner, K., Schmidt, C. and Situ, G. (2005) A Multiagent Framework to Integrate and Visualize Gene Expression Information. IEEE ICDM Workshop on MADW & MADM, 1-7.
http://www.eecis.udel.edu/~kamboj/pubs/kamboj.madw.05.pdf [7] Stiglic, G. and Kokol, P. (2004) Using Multi-Agent System for Gene Expression Classification. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, 1-5 September 2004, 2952-2955. [8] Wooldridge, M. (2002) An Introduction to Multiagent Systems. John Wiley, USA. [9] Laureano-Cruces, A.L., Ramírez-González, T., Sánchez-Guerrero, L. and Ramírez-Rodríguez, J. (2014) Multi-Agent System for Real Time Planning Using Collaborative Agents. International Journal of Intelligence Science, 4, 91-103. [10] Laureano-Cruces, A.L. and Espinoza-Paredes, G. (2005) Behavioral Design to Model a Reactive Decision of an Expert in Geothermal Wells. International Journal of Approximate Reasoning, 39, 1-28.
http://dx.doi.org/10.1016/j.ijar.2004.08.002 [11] Jennings, N. (2001) An Agent-Based Approach for Building Complex Software Systems. Communications of the ACM, 44, 35-41.
http://dx.doi.org/10.1145/367211.367250 [12] Koutkias, V., Malousi, A. and Maglaveras, N. (2007) Engineering Agent-Mediated Integration of Bioinformatics Analysis Tools. Multiagent and Grid Systems, 3, 245-258. [13] Kohonen, T. (1995) Self Organizing Maps. Springer, Berlin. [14] Espinosa, A., Alfaro, A., Roman-Basaure, E., Guardado-Estrada, M., Palma, í., Serralde, C., et al. (2013) Mitosis Is a Source of Potential Markers for Screening and Survival and Therapeutic Targets in Cervical Cancer. PLoS ONE, 8, e55975.
http://dx.doi.org/10.1371/journal.pone.0055975 [15] Karasavvas, K., Burger, A. and Baldock, R. (2004) Bioinformatics Integration and Agent Technology. Journal of Biomedical Informatics, 37, 205-219. http://dx.doi.org/10.1016/j.jbi.2004.04.003 [16] Clips (2014) http://clipsrules.sourceforge.net/ [17] Gentleman, R., Carey, V., Huber, W., Irizarry, R. and Dudoit, S. (2005) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer.
http://www.bioconductor.org/ [18] Márquez, E., Savage, J., Espinosa, A., Berumen, J. and Lemaitre, C. (2008) Gene Expression Analysis for Tumor Classification Using Vector Quantization. 3rd IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008), Melbourne, 15-17 October 2008, 95-103. [19] National Center for Biotechnology Information (2014) http://www.ncbi.nlm.nih.gov/ [20] DAVID Bioinformatics Resources (2014) http://david.abcc.ncifcrf.gov/ [21] Luck, M. and d’Inverno, M. (2001) A Conceptual Framework for Agent Definition and Development. The Computer Journal, 44, 1-20. [22] Sánchez-Guerrero, L., Laureano-Cruces, A.L., Mora-Torres, M., Ramírez-Rodríguez, J. and Silva-López, R. (2013) A Multi-Agent Intelligent Learning System: An Application with a Pedagogical Agent and Learning Objects. Creative Education, 4, 181-190.
http://www.scirp.org/journal/ce [23] Laureano-Cruces, A.L., Acevedo-Moreno, D.A., Mora-Torres, M. and Ramirez-Rodriguez, J. (2012) A Reactive Behavior Agent: Including Emotions for a Video Game. Journal of Applied Research and Technology (CCADET-UNAM), 10, 651-672. [24] Sánchez-Guerrero, L., Laureano-Cruces, A.L., Mora-Torres, M. and Ramìrez-Rodríguez, J. (2011) Multiagent Architecture for Errors Management in Content Organized in Learning Objects. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2011, Hawai, 17-21 October 2011, 2462-2467. www.EdiTLib.org [25] Laureano-Cruces, A.L. and Verduga-Palencia, D.O. (2010) Simulación de un juego de futbol utilizando una arquitectura Multiagente-Reactiva. In Libro: Desarrollo Tecnológico. (Alfa-Omega), XXIII Congreso Nacional y XI Congreso Internacional de Informática y Computación de la ANIEI, Puerto Vallarta, 11-15 de octubre, 485-493. [26] Berman, J. (2004) Tumor Classification: Molecular Analysis Meets Aristotle. BMC Cancer, 4, 10.