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

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. 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)
URI	https://reading-clone.eprints-hosting.org/id/eprint/4497
Identification Number/DOI	10.1109/IJCNN.2007.4371399
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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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