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.

ANTONINO FIANNACA, GIUSEPPE DI FATTA, RICCARDO RIZZO, ALFONSO URSO, GAGLIO S (2007). Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds. In Proc. of IEEE International Joint Conference on Neural Networks (pp.2776-2781). PISCATAWAY, NJ 08855-1331 : IEEE [10.1109/IJCNN.2007.4371399].

Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds

FIANNACA, Antonino;URSO, Alberto;GAGLIO, Salvatore
2007-01-01

Abstract

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.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
IEEE International Joint Conference on Neural Networks IJCNN2007
Orlando, Florida
12-17 Aug. 2007
2007
6
ANTONINO FIANNACA, GIUSEPPE DI FATTA, RICCARDO RIZZO, ALFONSO URSO, GAGLIO S (2007). Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds. In Proc. of IEEE International Joint Conference on Neural Networks (pp.2776-2781). PISCATAWAY, NJ 08855-1331 : IEEE [10.1109/IJCNN.2007.4371399].
Proceedings (atti dei congressi)
ANTONINO FIANNACA; GIUSEPPE DI FATTA; RICCARDO RIZZO; ALFONSO URSO; GAGLIO S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/4904
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