LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Fiannaca, Antonino; Di Fatta, Giuseppe; Rizzo, Riccardo; Urso, Alfonso; Gaglio, Salvatore (2007)
Languages: English
Types: Unknown
Subjects:
Visual exploration of scientific data in life science\ud area is a growing research field due to the large amount of\ud available data. The Kohonen’s Self Organizing Map (SOM) is\ud a widely used tool for visualization of multidimensional data.\ud In this paper we present a fast learning algorithm for SOMs\ud that uses a simulated annealing method to adapt the learning\ud parameters. The algorithm has been adopted in a data analysis\ud framework for the generation of similarity maps. Such maps\ud provide an effective tool for the visual exploration of large and\ud multi-dimensional input spaces. The approach has been applied\ud to data generated during the High Throughput Screening\ud of molecular compounds; the generated maps allow a visual\ud exploration of molecules with similar topological properties.\ud The experimental analysis on real world data from the\ud National Cancer Institute shows the speed up of the proposed\ud SOM training process in comparison to a traditional approach.\ud The resulting visual landscape groups molecules with similar\ud chemical properties in densely connected regions.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] M. Deshpande, M. Kuramochi, and G. Karypis, “Frequent sub-structurebased approaches for classifying chemical compounds,” Proceedings of IEEE International Conference on Data Mining (ICDM'03), Nov. 19- 22, 2003.
    • [2] G. Di Fatta, A. Fiannaca, R. Rizzo, A. Urso, M. R. Berthold and S. Gaglio, Context-Aware Visual Exploration of Molecular Databases, Workshops IEEE International Conference on Data Mining (ICDM 2006), dec 18-22, 2006, pp.136-141.
    • [3] Gasteiger, J.; Zupan, J., “Neural networks in chemistry,” Angew. Chem. Int. Ed. 32, pp. 503-527, 1993.
    • [4] Manallack, D.; Livingstone, D., “Neural networks in drug discovery: Have they lived upto their promise?” Eur. J. Med. Chem. 34, pp. 195- 208, 1999.
    • [5] Rose, V.; Croall, I.; Macfie, H., “An application of unsupervised neural network methodology kohonen topology-preserving mapping to qsar analysis,” Quant. Struct.-Act. Relat. 10, pp. 6-15, 1991.
    • [6] Bienfait, B., “Applications of high reolution self organizing maps to retrosynthetic and qsar analysis,” J. Chem. Inf. Comput. Sci. 34, pp. 890-898, 1994.
    • [7] Tetko, I.; Kovalishyn, V.; Livingstone, D., “Volume learning algorithm artificial neural networks for 3d qsar studies,” J. Med. Chem. 44, pp. 2411-2420, 2001.
    • [8] Espinosa, G.; Arenas, A.; Giralt, F., “An integrated som fuzzy artmap neural system for the evaluation of toxicity,” J. Chem. Inf. Comput. Sci. 42, pp. 343-359, 2002.
    • [9] Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E. S., Golub T. R., “Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation,” Proceedings of the National Academy Science of USA 96, pp. 2907-2912, 1999.
    • [10] Nikkila J., and Toronen P. and Kaski S. and Venna J. and Castren E. and Wong G., “Analysis and visualization of gene expression data using self-organizing maps,” Neural Networks, 15.
    • [11] K. Haese, ”Kalman filter implementation of self-organizing feature maps,” Neural Comput., vol. 11, no. 5, pp. 1211-1233, 1999.
    • [12] Ultsch, A., “Self-organizing neural networks for visualization and classification,” In O. Opitz, B. Lausen, & R. Klar (Eds.), Information and Classification.
    • [13] R. Agrawal, T. Imielinski, and A. N. Swami, “Mining association rules between sets of items in large databases,” Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207- 216, May 26-28, 1993.
    • [14] M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, “New algorithms for fast discovery of association rules,” Proceedings of 3rd Int. Conf. on Knowledge Discovery and Data Mining (KDD'97), pp. 283-296, 1997.
    • [15] C. Borgelt and M. R. Berthold, “Mining molecular fragments: Finding relevant substructures of molecules,” IEEE International Conference on Data Mining (ICDM 2002), pp. 51-58, Dec. 9-12, 2002.
    • [16] T. Graepel, M. Burger and K. Obermayer, “Self-organizing maps: generalizations and new optimization techniques”, Neurocomputing, vol.21, pp. 173-190, 1998.
    • [17] J. K. Cullum, R. A. Willoughby, Real rectangular matrices, in Lancozos algorithms for large symmetric eigenvalue computations, Vol. 1 Theory. Boston: Brikhauser, 1985.
    • [18] T. Kohonen, Self-Organizing Maps. Berlin: Springer Verlag, 1995.
    • [19] Kaski, S., Kangas, J., & Kohonen, T., “Bibliography of selforganizing map (som) papers: 1981-1997. neural computing surveys, 1(3&4), 1-176,” available in electronic form at http://www. icsi.berkeley.edu/ jagota/NCS/: 1 102-350.
    • [20] Ultsch A. and Morchen F., “Esom-maps: tools for clustering, visualization, and classification with emergent som,” Technical Report 46, CS Department, Philipps-University Marburg, 2005.
    • [21] T. Heskes. “Energy functions for self-organizing maps”, In E. Oja and S. Kaski, editors, Kohonen Maps, pages 303316. Elsevier, Amsterdam, 1999.
    • [22] National Cancer Institute, “DTP AIDS antiviral screen dataset [online]. http://dtp.nci.nih.gov/docs/aids/aids/data.html.”
    • [23] O. Weislow, R. Kiser, D. Fine, J. Bader, R. Shoemaker, and M. Boyd, “New soluble formazan assay for hiv-1 cytopathic effects: Application to high flux screening of synthetic and natural products for aids antiviral activity,” Journal of the National Cancer Institute, University Press, Oxford, United Kingdom, vol. 81, pp. 577-586, 1989.
    • [24] N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21 (6), 1087-1092, 1953.
    • [25] J. Goppert, W. Rosenstiel, Regularized SOM-Training: A Solution to the Topology-Approximation dilemma, ICNN96,New York, pp. 38-43, 1996.
    • [26] E. Berglund, J. Sitte, ”The parameterless self-organizing map algorithm”, IEEE Transactions on neural networks, vol. 17, no. 2, pp. 305- 316, 2006.
    • [27] J.-C. Fort, P. Letrmy, M. Cottrell, “Advantages and drawbacks of the batch Kohonen algorithm”, European Symposium on Artificial Neural Networks , Bruges, Belgium. pp. 223-230, 2002.
    • [28] R. Rizzo, A. Chella, ”A Comparison between Habituation and Conscience Mechanism in Self-Organizing Maps”, IEEE Transactions on neural networks, vol. 17, no. 3, pp. 807-810, 2006.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article