Abstract
This study applies the Support Vector Machine (SVM) algorithm to the problem of chemical compound screening with a desired activity and hit definition. The problem of automatically tuning multiple parameters for pattern recognition SVMs using our new introduced kernel for chemical compounds is considered. This is done by a simple eigen analysis method which is applied to the matrix of the same dimension as the kernel matrix to find the structure of feature data, and to find the kernel parameter accordingly. We characterize distribution of data by the principle component analysis method.
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References
Burden, F.R. 1989. ”Molecular Identification Number For Substructure Searches”, Journal of Chemical Information and Computer Sciences, 29, 225–227.
Graham W. Richards. Virtual screening using grid computing: the screensaver project. Nature Reviews: Drug Discovery, 1:551–554, July 2002.
H. Froehlich, J. K. Wegner, A. Zell, QSAR Comb. Sci. 2004, 23, 311–318.
K.-M. Chung, W.-C. Kao, T. Sun, L.-L. Wang, and C.-J. Lin, “Radious margin bounds for support vector machines with Gaussian kernel”, Neural Computation, vol. 15, pp. 2643–2681, 2003.
Kozak K., Kozak M., Stapor K., Kernels for chemical compounds in biological screening. ICANNGA 2007, Springer-Verlag, LNCS/LNAI Proceedings
Pearlman, R.S. Smith, K. M. 1998.“Novel software tools for chemical diversity”, Perspectives in Drug Discovery and Design, 9/10/11, 339–353.
R. Debnath, H. Takahashi, “Generalization of kernel PCA and automatic parameter tun-ing”, Neural Information Processing, Vo. 5, No. 3, December 2004.
S. S. Keerthi C.-J. Lin, “Asymptotic behaviours of support vector machines with Gaussian kernel”, Neural Computation, vol. 15, pp. 1667–1689, 2003.
S. Kramer, L. De Raedt, C. Helma. Molecular feature mining in hiv data. In 7th Inter-national Conference on Knowledge Discovery and Data Mining, 2001.
Schoelkopf, B., and Smola, A. J. (2002). Learning with kernels. Cambridge, MA, MIT Press.
V. N. Vapnik, Stasistical Learning Theory. New York: Wiley, 1998.
V. Vapnik O. Chapelle, “Bounds on error expectation for support vector machines”, Neural computation, vol. 12, no. 9, pp. 2013–2036, 2000.
Westfall, P. H. Young, S. S.(1993). Resampling-based multiple testing: Ex-amples and methods for p-value adjustment, John Wiley and Sons.
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Kozak, K., Kozak, M., Stapor, K. (2007). Optimization of the SVM Screening Kernel-Application to Hit Definition in Compound Screening. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_28
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DOI: https://doi.org/10.1007/978-3-540-75175-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75174-8
Online ISBN: 978-3-540-75175-5
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