7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009) pp 217-226 | Cite as
Multiagent Systems in Expression Analysis
- 2 Citations
- 699 Downloads
Abstract
This paper presents a multiagent system for decision support in the diagnosis of leukemia patients. The core of the system is a type of agent that integrates a novel strategy based on a case-based reasoning mechanism to classify leukemia patients. This agent is a variation of the CBP agents and proposes a new model of reasoning agent, where the complex processes are modeled as external services. The agents act as coordinators of Web services that implement the four stages of the case-based reasoning cycle. The multiagent system has been implemented in a real scenario, and the classification strategy includes a novel ESOINN neuronal network and statistics methods to analyze the patient’s data. The results obtained are presented within this paper and demonstrate the effectiveness of the proposed agent model, as well as the appropriateness of using multiagent systems to resolve medical problems in a distributed way.
Keywords
Multiagent Systems Case-Based Reasoning microarray neuronal network ESOINN Case-based planningPreview
Unable to display preview. Download preview PDF.
References
- 1.Lander, E., et al.: Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001)CrossRefGoogle Scholar
- 2.Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 3.Glez-Bedia, M., Corchado, J.: A planning strategy based on variational calculus for deliberative agents. Computing and Information Systems Journal 10(1), 2–14 (2002)Google Scholar
- 4.Furao, S., Ogura, T., Hasegawa: An enhanced self-organizing incremental neural network for online unsupervised learning. Neural Networks 20, 893–903 (2007)zbMATHCrossRefGoogle Scholar
- 5.Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K., Scherf, U., Speed, T.: Exploration, Normalization, and Summaries of High density Oligonucleotide Array Probe Level Data. Biostatistics 4, 249–264 (2003)zbMATHCrossRefGoogle Scholar
- 6.Brunelli, R.: Histogram Analysis for Image Retrieval. Pattern Recognition 34, 1625–1637 (2001)zbMATHCrossRefGoogle Scholar
- 7.Shen, F.: An algorithm for incremental unsupervised learning and topology representation. Ph.D. thesis. Tokyo Institute of Technology, Tokyo (2006)Google Scholar
- 8.Gariepy, R., Pepe, W.: On the Level sets of a Distance Function in a Minkowski Space. Proceedings of the American Mathematical Society 31(1), 255–259 (1972)Google Scholar
- 9.Breiman, L., Friedman, J., Olshen, A., Stone, C.: Classification and regression trees. In: Wadsworth International Group, Belmont, California (1984)Google Scholar
- 10.Quinlan, J.: Discovering rules by induction from large collections of examples. In: Michie, D. (ed.) Expert systems in the micro electronic age, pp. 168–201. Edinburgh University Press, Edinburgh (1979)Google Scholar
- 11.Chua, A., Ahna, H., Halwanb, B., Kalminc, B., Artifond, E., Barkune, A., Lagoudakisf, M., Kumar, A.: A decision support system to facilitate management of patients with acute gastrointestinal bleeding. Artificial Intelligence in Medicine 42(3), 247–259 (2008)CrossRefGoogle Scholar
- 12.François, P., Cremilleux, B., Robert, C., Demongeot, J.: MENINGE: A medical consulting system for child’s meningitis. Study on a series of consecutive cases. Artificial Intelligence in Medicine. 32(2), 281–292 (1992)CrossRefGoogle Scholar
- 13.Quackenbush, J.: Computational analysis of microarray data. Nature Review Genetics 2(6), 418–427 (2001)CrossRefGoogle Scholar
- 14.Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology Of The Grid: An Open Grid Services Architecture For Distributed Systems Integration. Technical Report of the Global Grid Forum (2002)Google Scholar
- 15.Leake, D., Kendall-Morwick, J.: Towards case-based support for e-science workflow generation by mining provenance. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 269–283. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 16.Shinawi1, M., Cheung, S.W.: The array CGHnext term and its clinical applications. Drug Discovery Today 13(17-18), 760–770 (2008)CrossRefGoogle Scholar
- 17.
- 18.Wooldridge, M., Jennings, N.: Agent Theories, Architectures, and Languages: a Survey. In: Wooldridge, Jennings (eds.) Intelligent Agents, pp. 1–22. Springer, Berlin (1995)Google Scholar
- 19.Erl, T.: Service-Oriented Architecture (SOA): Concepts, Technology, and Design. Prentice Hall PTR, Englewood Cliffs (2005)Google Scholar
- 20.Vittorini, P., Michettia, M., di Orio, F.: A SOA statistical engine for biomedical data. Computer Methods and Programs in Biomedicine 92(1), 144–153 (2008)CrossRefGoogle Scholar
- 21.Saitou, N., Nie, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4, 406–425 (1987)Google Scholar
- 22.Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics (1990)Google Scholar
- 23.Stevens, R., McEntireb, G.C., Greenwooda, M., Zhaoa, J., Wipatc, A., Lic, P.: MyGrid and the drug discovery process. Drug Discovery Today: BIOSILICO 2(4), 140–148 (2004)CrossRefGoogle Scholar
- 24.Huhns, M., Singh, M.P.: Service-Oriented Computing: Key Concepts and Principles. Internet Computing 9(1), 75–81 (2005)CrossRefGoogle Scholar
- 25.Arranz, A., Cruz, A., Sanz-Bobi, M.A., Ruíz, P., Coutiño, J.: DADICC: Intelligent system for anomaly detection in a combined cycle gas turbine plant. Expert Systems with Applications 34(4), 2267–2277 (2008)CrossRefGoogle Scholar
- 26.Contreras, M., Sheremetov, L.: Industrial application integration using the unification approach to agent-enabled semantic SOA. Robotics and Computer-Integrated Manufacturing 24(5), 680–695 (2008)CrossRefGoogle Scholar
- 27.Tapia, D.I., Rodriguez, S., Bajo, J., Corchado, J.M.: FUSION@, A SOA-Based Multi-agent Architecture. In: International Symposium on Distributed Computing and Artificial Intelligence, Advances in Soft Computing, vol. 50, pp. 99–107 (2008)Google Scholar