Comparison of MLP NN Approach with PCA and ICA for Extraction of Hidden Regulatory Signals in Biological Networks

Document Type: Research Article

Authors

1 Department of Chemical Engineering, Amirkabir University of Technology, Tehran, I.R. IRAN

2 Department of Computer Engineering & Information Technology, Amirkabir University of Technology, Tehran, I.R. IRAN

Abstract

The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. So far, many computational and statistical methods such as PCA and ICA have been employed for computing low-dimensional or hidden representations of these datasets, but in most cases the results are inconsistent with underlying real network. In this paper we have employed and compared three linear (PCA and ICA) and non-linear (MLP neural network) dimensionality reduction techniques to uncover these regulatory signals, from outputs of such networks. The three approaches were verified experimentally using the absorbance spectra of a network of seven hemoglobin solutions, and the results revealed the superiority of the MLP NN to PCA and ICA. This study shows the capability of the MLP NN approach to efficiently determine the regulatory components in biological networked systems.

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[1] Raychaudhuri, S., Stuart, J. M. and Altman, R. B., Principal components analysis to summarize microarray experiments: application to sporulation time series, Pac. Symp. Biocomput., 5, 455 (2000).

[2] Holter, N. S., Mitra, M., Maritan, A., Cieplak, M., Banavar, J. R. and Fedoroff, N. V., Fundamental patterns underlying gene expression profiles: simplicity from complexity,  Proc. Natl. Acad. Sci. USA,97, 8409 (2000).

[3] Holter, N. S., Maritan, A., Cieplak, M., Fedoroff, N. V. and Banavar, J. R., Dynamic modeling of gene expression data, Proc. Natl. Acad. Sci. USA, 98, 693 (2001).

[4] Yeung, M. K., Tegner, J. and Collins, J. J., Reverse engineering gene networks using singular value decomposition and robust regression,  Proc. Natl. Acad. Sci. USA,99, 6163 (2002).

[5] Liebermeister, W., Linear models of gene expression determined by independent component analysis, Bioinformatics, 18, 51 (2002).

[6] Liao, James C., Riccardo Boscolo Young-Lyeol Yang, Linh My Tran, Chiara Sabatti, and Vwani P. Roychowdhury, Network component analysis: Reconstruction of regulatory signals in biological systems, PNAS, 100 (26), 15522, December 23
( 2003).

[7] Lay, D. C., Linear Algebra And Its Applications, 2nd ed.; Addison-Wesley Longman Inc.: Reading, MA (1997).

[8] Hyvarinen, A., Oja, E., Independent component analysis: algorithms and applications, Neural Networks, 13, 411 (2000).

[9] Hyvarinen, A., and Oja, E., A fast fixed-point algorithm for independent component analysis, Neural Computation, 9 (7), 1483 (1997).

[10] Yamaguchi, T., Itoh1, K., An algebraic solution to independent component analysis, Optics Communi-cations, 178, 59 (2000).

[11] Karhunen, J., Oja, E., Wang, L., Vigario, R. and Joutsensalo, J., A class of neural networks for independent component analysis, IEEE Trans. Neural Networks, 8, 486 (1997).

[12] Haykin, S.,  Neural Networks: A  Comprehensive Foundation, Prentice Hall (1999).

[13] Mansour, D., and Juang, B.H., A family of distortion measures based upon projection operation of robust speech recognition, IEEE Trans. Acoustics, Signal and Speech Processing, ASSP, 57(11), 659 (1989).

[14] Lee, T. I., et al.,Transcriptional regulatory networks in Saccharomyces cerevisiae, Science,298, 799 (2002).

[15] Gardner,  T. S., di Bernardo, D.,  Lorenz, D. and Collins, J. J., Inferring genetic networks and identifying compound mode of action via expression profiling,  Science  301, 102 (2003).

[16] Iyer, V. R., Horak, C. E., Scafe, C. S., Botstein, D., Snyder, M., and Brown, P. O., Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF, Nature,409,  533 (2001).