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Fast training of self organizing maps for the visual exploration of molecular compounds

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Fiannaca, A., Di Fatta, G., Rizzo, R., Urso, A. and Gaglio, S. (2007) Fast training of self organizing maps for the visual exploration of molecular compounds. In: Int.l Joint Conference on Neural Networks (IJCNN), Aug. 12-17, 2007, Orlando, Florida, pp. 2776-2781.

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To link to this article DOI: 10.1109/IJCNN.2007.4371399

Abstract/Summary

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:4497
Uncontrolled Keywords:biochemistry , biology computing , data analysis , data visualisation , learning (artificial intelligence) , molecular biophysics , self-organising feature maps , simulated annealing Kohonen's self organizing map , data analysis framework , fast learning algorithm , high throughput screening , molecular compound , scientific data visualisation , self organizing map , simulated annealing method

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