Abstract
Triple negative breast cancer (TNBC) is an aggressive form of breast cancer. Despite treatment with chemotherapy, relapses are frequent and response to these treatments is not the same in younger women as in older women. Therefore, the identification of genes that provoke this disease is required, as well as the identification of therapeutic targets. There are currently different hybridization techniques, such as expression arrays, which measure the signal expression of both the genomic and transcriptomic levels of thousands of genes of a given sample. Probesets of Gene 1.0 ST GeneChip arrays provide the ultimate genome transcript coverage, providing a measurement of the expression level of the sample. This paper proposes a multi-agent system to manage information of expression arrays, with the goal of providing an intuitive system that is also extensible to analyze and interpret the results. The roles of agent integrate different types of techniques, from statistical and data mining techniques that select a set of genes, to search techniques that find pathways in which such genes participate, and information extraction techniques that apply a CBR system to check if these genes are involved in the disease.
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This work has been c has been supported by the Spanish Government through the project iHAS (grant TIN2012-36586-C01/C02/C03) and FEDER funds.
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González, A., Ramos, J., De Paz, J.F., Corchado, J.M. (2015). Obtaining Relevant Genes by Analysis of Expression Arrays with a Multi-agent System. In: Overbeek, R., Rocha, M., Fdez-Riverola, F., De Paz, J. (eds) 9th International Conference on Practical Applications of Computational Biology and Bioinformatics. Advances in Intelligent Systems and Computing, vol 375. Springer, Cham. https://doi.org/10.1007/978-3-319-19776-0_15
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