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The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Fiannaca, A., La Rosa, M., Di Fatta, G., Gaglio, S., Rizzo, R. and Urso, A. (2014) The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration. Journal of Cheminformatics, 6 (1). 24. ISSN 1758-2946

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To link to this item DOI: 10.1186/1758-2946-6-24

Abstract/Summary

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:39802
Uncontrolled Keywords:Molecular compounds, Self organizing map, Clustering, Visualization, Taverna

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