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

Research areas:

• Uncategorized

Year:

2007

Type of Publication:

In Proceedings

Authors:

• Antonino Fiannaca
• Giuseppe DiFatta
• Riccardo Rizzo
• Alfonso Urso

Book title:

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Pages:

2776-2781

Month:

August

ISBN:

978-1-4244-1379-9

ISSN:

1098-7576

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.