Abstract
Two artificial neural networks of different types were applied to gene expression profiles in glioblastoma, the most aggressive human brain tumor, and in normal brain tissue. The results of gene expression profiles classification are presented. First method, self organizing maps, gave good discrimination of profiles on the trained map. Another ANN, perceptron, showed a good result of classificatio — more then 95% of the test data set were successfully classified. Due to high correlations between some gene expression values one can suppose, that number of genes necessary for successful classification may be reduced.
Similar content being viewed by others
References
Kavsan, V.M., Shostak, K.O., Dmitrenko, V.V., Zozulya, Y.A., Rozumenko, V.D., and Demotes-Mainard, J., Characterization of Genes with Increased Expression in Human Glioblastomas, Tsitol Genet., 2005, vol. 39, pp. 37–49.
van De Veer, L.J., Dai, H., van De Vijver, M.J., He, Y.D., Hart, A.A.M., Mao, M., Peterse, J.L., van Der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., and Friend, S.H., Gene Expression Profiling Predicts Plinical Outcome of Breast Cancer, Nature, 2002, vol. 415, pp. 530–536.
Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J., Jatkoe, T., Berns, E.M., Atkins, D., and Foekens, J.A., Gene-Expression Profiles To Predict Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer, Lancet, 2005, vol. 365, pp. 671–679.
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F.L., Walker, M.G., Watson, D., Park, T., Hiller, W., Fisher, E.R., Wickerham, D.L., Bryant, J., and Wolmark, N., A Multigene Assay To Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer, N. Engl. J. Med., 2004, vol. 351, pp. 2817–2826.
Ma, X.J., Hilsenbeck, S.G., Wang, W., Ding, L., Sgroi, D.C., Bender, R.A., Osborne, C.K., Allred, D.C., Erlander, M.G., The HOXB13: IL17BR Expression Index is a Prognostic Factor in Early-Stage Breast Cancer, J. Clin. Oncol., 2006, vol. 24, pp. 4611–4619.
Venu Gopala Rao, K., Prem Chand, P., and Ramana Murthy, M.V., A Neural Network Approach in Medical Decision Systems, Journal of Theoretical and Applied Information Technology, 2007, vol. 3, no. 4, pp. 97–101.
Törönen, P., Kolehmainen, M., Wong, G., Castrén, E., Analysis of Gene Expression Data using Self-Organizing Maps, FEBS Letters, 1999, vol. 451, pp. 142–146.
Granzow, M., Berrar, D., Dubitzky, W., Schuster, A., Azuaje, F.J., and Eils, R., Tumor Classification By Gene Expression Profiling: Comparison and Validation of Five Clustering Methods, ACM SIGBIO Newsletter, 2001, vol. 21, no. 1, pp. 16–22,.
Demuth, T., Rennert, J.L., Hoelzinger, D.B., Reavie, L.B., Nakada, M., Beaudry, C., Nakada, S., Anderson, E.M., Henrichs, A.N., McDonough, W.S., Holz, D., Joy, A., Lin, R., Pan, K.H., Lih, C.J., Cohen, S.N., and Berens, M.E., Glioma Cells on The Run — The Migratory Transcriptome of 10 Human Glioma Cell Lines, BMC Genomics, 2008, vol. 9, p. 54
Li, A., Walling, J., Ahn, S., Kotliarov, Y., Su, Q., Quezado, M., Oberholtzer, J.C., Park, J., Zenklusen, J.C., and Fine, H.A., Unsupervised Analysis of Transcriptomic Profiles Reveals Six Glioma Subtypes, Cancer Res., 2009, vol. 69, no. 5, pp. 2091–9.
Petalidis, L.P., Oulas, A., Backlund, M., Wayland, M.T., Liu, L., Plant, K., Happerfield, L., Freeman, T.C., Poirazi, P., and Collins, V.P., Improved grading and Survival Prediction of human Astrocytic Brain Tumors By Artificial Neural Network Analysis of Gene Expression Microarray Data, Mol Cancer Ther May 2008, vol. 7, pp. 1013–1024.
Deboeck, G., Kohonen, T., Eds., Visual Explorations in Finance with Self-Organizing Maps, London: Springer-Verlag, 1998.
Laboratory of Computer and information Science Adaptive Informatics Research Centre, Projects: SOM-PAK, WEBSOM, Toolbox MatLab, www.cis.fi
ESOM — DataBionics. Marburg, http://www.mathematik.uni-marburg.de
Gorban, B., Kegl, D., Wunsch, A., and Zinovyev, Eds., Principal Manifolds For Data Visualization and Dimension Reduction, Berlin — Heidelberg — New York: Springer, 2007.
Viscovery SOMMine — Eudaptics Software Viscovery SOMine, www.eudaptics.com
Ellipse. Ellipse Self Organizing Maps. www.ellipse.fi
Schwartz, D.R., Algorithmic Peculiarities of Multidimensional Data Clusterization Method, Based on Kohonen Networks, Science and Innovations in The Technical Universities, St.Petersburg, 2007, pp. 90–93 (in Russian).
Author information
Authors and Affiliations
Corresponding author
Additional information
The article is published in the original.
An erratum to this article can be found at http://dx.doi.org/10.3103/S1060992X10040132
About this article
Cite this article
Mekler, A.A., Knyazeva, I., Schwartz, D.R. et al. 47Glioblastoma gene expression profile diagnostics by the artificial neural networks. Opt. Mem. Neural Networks 19, 181–186 (2010). https://doi.org/10.3103/S1060992X10020098
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S1060992X10020098