Abstract
In this paper a novel gene selection method based on personalized modeling is proposed and is compared with classical machine learning techniques to identify diagnostic gene targets and to use them for a successful diagnosis of a medical problem - acute graft-versus-host disease (aGvHD). An analysis using the integrated approach of new data with the existing models is evaluated. Identifying a compact set of genes from gene expression data is a critical step in bioinformatics research. Personalized modeling is a recently introduced technique for constructing clinical decision support systems. This is a novel study which utilises both computational and biological evidence and the use of a personalized modeling for the analysis of this disease. Directions for further studies are also outlined.
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References
Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach, 2nd edn. Springer, London (2007)
Weisdorf, D.: Graft vs. Host disease: pathology, prophylaxis and therapy: GVHD overview. Best Pr. & Res. Cl. Haematology 21(2), 99–100 (2008)
Hu, Y., Song, Q., Kasabov, N.: Personalized modeling based gene selection for microarray data analysis. In: Koeppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5506, pp. 1221–1228. Springer, Heidelberg (2009)
Kasabov, N.: Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach. Pattern Recognition Letters 28(6), 673–685 (2007)
Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evolutionary Computation 3(4), 287–297 (1999)
Fiasché, M., Verma, A., Cuzzola, M., Iacopino, P., Kasabov, N., Morabito, F.C.: Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5769, pp. 10–19. Springer, Heidelberg (2009)
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods–Support Vector Learning. MIT Press, Cambridge (1998)
Foley Jason, J.E., Mariotti, J., Ryan, K., Eckhaus, M., Fowler, D.H.: The cell therapy of established acute graft-versus-host disease requires IL-4 and IL-10 and is abrogated by IL-2 or host-type antigen-presenting cells. Biology of Blood and Marrow Transplantation 14, 959–972 (2008)
Fiasché, M.: Implementations of Evolving Integrated Multimodel Systems, Algorithms and Applications in Biomedical Field. PhD Thesis. DIMET, University “Mediterranea” of Reggio Calabria (2010)
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Fiasché, M., Cuzzola, M., Fedele, R., Iacopino, P., Morabito, F.C. (2010). Machine Learning and Personalized Modeling Based Gene Selection for Acute GvHD Gene Expression Data Analysis. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_29
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DOI: https://doi.org/10.1007/978-3-642-15819-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15818-6
Online ISBN: 978-3-642-15819-3
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