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
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