Abstract
Gene expression profiling strategies have attracted considerable interest from biologist due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Methods using artifical neural networks (ANNs) were developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. The problematic of using a stepwise forward selection ANN method is that it needs many different parameters depending on the complexity of the problem and choosing the proper neural network architecture for a given classification problem is not a trivial problem. A novel constructive neural networks algorithm (CMantec) is applied in order to predict estrogen receptor status by using data from microarrays experiments. The obtained results show that CMantec model clearly outperforms the ANN model both in process execution time as in the final prognosis accuracy. Therefore, CMantec appears as a powerful tool to identify gene signatures that predict the ER status for a given patient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Andree, H.M.A., Barkema, G.T., Lourens, W., Taal, Vermeulen, J.C.: A comparison study of binary feedforward neural networks and digital circuits. Neural Networks 6, 785–790 (1993)
Baum, E.B., Haussler, D.: What size net gives valid generalization? Neural Computation 1, 151–160 (1989)
Frean, M.: The upstart algorithm: A method for constructing and training feedforward neural networks. Neural Computation 2, 198–209 (1990)
Frean, M.: Thermal perceptron learning rule. Neural Computation 4, 946–957 (1992)
Gómez, I., Franco, L., Jerez, J.M.: Neural Network Architecture Selection: Can function complexity help? Neural Processing Letters (in press, 2009) doi:10.1007 s11063-009-9108-2
Keibek, S.A.J., Barkema, G.T., Andree, H.M.A., Savenlie, M.H.F., Taal, A.: A fast partitioning algorithm and a comparison of binary feedforward neural networks. Europhys. Lett. 18, 555–559 (1992)
Lancashire, L.J., Rees, R.C., Ball, G.R.: Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artifical neural network modelling approach. Artificial Intelligence in Medicine 43, 99–111 (2008)
Lawrence, S., Giles, C.L., Tsoi, A.: What Size Neural Network Gives Optimal Gener- alization? Convergence Properties of Backpropagation. Technical Report UMIACS-TR-96-22 and CS-TR-3617, University of Maryland (1996)
Mezard, M., Nadal, J.P.: Learning in feedforward layered networks: The tiling algorithm. J. Physics A 22, 2191–2204 (1989)
Nicoletti, M.C., Bertini, J.R.: An empirical evaluation of constructive neural network algorithms in classification tasks. International Journal of Innovative Computing and Applications 1, 2–13 (2007)
Parekh, R., Yang, J., Honavar, V.: Constructive Neural-Network Learning Algorithms for Pattern Classification. IEEE Transactions on Neural Networks 11, 436–451 (2000)
García-Pedrajas, N., Ortiz-Boyer, D.: A cooperative constructive method for neural networks for pattern recognition. Pattern Recognition 40, 80–98 (2007)
Pellagatti, A., Vetrie, D., Langford, C.F., Gama, S., Eagleton, H., Wainscoat, J.S., Boultwood, J.: Gene Expression Profiling in Polycythemia Vera Using cDNA Microarray Technology. Cancer Res. 63, 3940–3944 (2003)
Linder, R., Richards, T., Wagner, M.: Microarray data classified by artificial neural networks. Methods Mol. Biol. 382, 345–372 (2007)
Rosenhlatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 386–408 (1959)
Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by backpropagating errors. In: Rumelhart, D., Mc-Clelland, J. (eds.) Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, pp. 318–362. MIT Press, Cambridge (1986)
Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Subirats, J.L., Franco, L., Gòmez, I., Jerez, J.M.: Computational capabilities of feedforward neural networks: the role of the output function. In: Proceedings of the XII CAEPIA’07, vol. II, pp. 231–238 (2008) ISBN: 978-84-611-8848-2
Subirats, J.L., Jerez, J.M., Franco, L.: A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions. IEEE Transactions on Circuits and Systems I 55, 3188–3196 (2008)
Subirats, J.L., Franco, L., Molina, I., Jerez, J.M.: Competition and Stable Learning for Growing Compact Neural Architectures with Good Generalization Abilities: The C-Mantec Algorithm (2009) (sent for publication)
Utgoff, P.E., Stracuzzi, D.J.: Many-Layered Learning. Neural Computation 14, 2497–2539 (2002)
Wei Jun, S., Greer Braden, T., Frank, W., Steinberg Seth, M., Chang-Gue, S., et al.: Prediction of Clinical Outcome Using Gene Expression Profiling and Artificial Neural Networks for Patients with Neuroblastom. Cancer Res. 64, 6883–6891 (2004)
West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., et al.: Predicting the clinical status of human breast cancer by using genes expression profiles. Proc. Natl. Acad. Sci. U.S.A. 98, 11462–11467 (2001)
Xu, Y., Selaru, F.M., Yin, J., Zou, T.T., Shustova, V., Mori, Y., Sato, F., et al.: Prediction of Clinical Outcome Using Gene Expression Profiling and Artificial Neural Networks for Patients with Neuroblastom. Cancer Res. 62, 3493–3497 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Urda, D., Subirats, J.L., Franco, L., Jerez, J.M. (2010). Constructive Neural Networks to Predict Breast Cancer Outcome by Using Gene Expression Profiles. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-13022-9_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13021-2
Online ISBN: 978-3-642-13022-9
eBook Packages: Computer ScienceComputer Science (R0)