Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30178
Data Mining Classification Methods Applied in Drug Design

Authors: Mária Stachová, Lukáš Sobíšek

Abstract:

Data mining incorporates a group of statistical methods used to analyze a set of information, or a data set. It operates with models and algorithms, which are powerful tools with the great potential. They can help people to understand the patterns in certain chunk of information so it is obvious that the data mining tools have a wide area of applications. For example in the theoretical chemistry data mining tools can be used to predict moleculeproperties or improve computer-assisted drug design. Classification analysis is one of the major data mining methodologies. The aim of thecontribution is to create a classification model, which would be able to deal with a huge data set with high accuracy. For this purpose logistic regression, Bayesian logistic regression and random forest models were built using R software. TheBayesian logistic regression in Latent GOLD software was created as well. These classification methods belong to supervised learning methods. It was necessary to reduce data matrix dimension before construct models and thus the factor analysis (FA) was used. Those models were applied to predict the biological activity of molecules, potential new drug candidates.

Keywords: data mining, classification, drug design, QSAR

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329402

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2293

References:


[1] A.Hoeben, B.Landuyt, M. S. Highley, H.Wildiers, A. T. Van Oosterom, and E. A. De Bruijn,"Vascular Endothelial Growth Factor and Angiogenesis," Pharmacological Reviews, vol. 56 no. 4, pp. 549-580, Dec. 2004.
[2] Boh├í─ì A., Faculty of Natural Science, Comenius University in Bratislava, [email protected], private communication, 2009.
[3] DRAGON Professional verzion 5.5 2007,TALETE, srl.
[4] J. J. Irwin, T. Sterling, M. M. Mysinger, E. S. Bolstad, and R. G. Coleman, "ZINC--a free database of commercially available compounds for virtual screening,"J. Chem. Inf. Model., 2012, accepted for publication.
[5] W. Härdle, and L. Simar,Applied Multivariate Statistical Analysis.New York: Springer, Berlin, 2007.
[6] IBM SPSS Statistics, Help, Algorithms
[online]. On-line manual.
[cit. 2011-01-16], 127.0.0.1:4004/help/index.jsp?topic=/com.ibm.spss.statistics.help/alg_in troduction.htm.
[7] J. K. Vermunt, and J. Magidson,Technical Guide for Latent GOLD 4.0: Basic and Advanced
[online]. Statistical Innovations Inc., Belmont Massachusetts, 2005,
[cit. 2011-01-16]. www.statisticalinnovations.com/products/LGtechnical.pdf.
[8] J. K.Vermunt, and J. Magidson,"Latent class cluster analysis,"J. A. Hagenaars, A. L. McCutcheon (eds.). Applied Latent Class Analysis. Cambridge : Cambridge University Press, pp. 89-106,2002.
[9] A. Liaw, and M. Wiener,"Classification and Regression by randomForest," R News, vol. 2, no.3, pp. 18ÔÇö22,2002.
[10] A.Gelman, Y. S. Su, M.Yajima, J. Hill, M. G.Pittau, J. Kerman, and T. Zheng, "arm: Data Analysis Using Regression and Multilevel/Hierarchical Models," R package version 1.5-02.://CRAN.Rproject. org/package=arm,2012.
[11] L. Breiman, J.H. Friedman, R.A. Olshen, andC.J. Stone,Classification and Regression Trees.Chapman and Hall,Wadsworth, Inc., New York, 1984.
[12] StatSoft, Inc. Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/, 2011.
[13] L. Breiman, " Random Forests,"in Machine Learning, vol. 45, pp. 5-32, 2001.
[14] Ho Tin Kam,"Random Decision Forest," in.Proc. of the 3rd Int-l Conf. on Document Analysis and Recognition, Montreal, Canada, August 14- 18, pp. 278-282, 1995.
[15] T. Hastie, R.Tibshirani, and J. H. Friedman,The elements of statistical learning: data mining, inference, and prediction. New York: Springer- Verlag, 2001.
[16] A.Gelman, A.Jakulin, M. G.Pittau, and Y.S. Su, "A weakly informative default prior distribution for logistic and other regression models," The annals of Applied Statistics, vol. 2, no. 4, pp.1360-1383, 2008.