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A Graphical Tool for the Exploration and Visual Analysis of Biomolecular Networks

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 11925)


Many interactions among bio-molecular entities, e.g. genes, proteins, metabolites, can be easily represented by means of property graphs, i.e. graphs that are annotated both on the vertices (e.g. entity identifier, Gene Ontology or Human Phenotype Ontology terms) and on the edges (the strength of the relationship, the evidence of the source from which the weight has been taken, etc.). These graphs contain a relevant information that can be exploited for conducting different kinds of analysis, such as automatic function prediction, disease gene prioritization, drug repositioning. However, the number and size of the networks are becoming quite large and there is the need of tools that allow the biologists to manage the networks, graphically explore their structures, and organize the visualization and analysis of the graph according to different perspectives. In this paper we introduce the web service that we have developed for the visual analysis of biomolecular networks. Specifically we will show the different functionalities for exploring big networks (that do not fit in the current canvas) starting from a specific vertex, for changing the view perspective of the network, and for navigating the network and thus identifying new relationships. The proposed system extends the functionalities of off-the-shelf graphical visualization tools (e.g. GraphViz and GeneMania) by limiting the production of big cloud of points and allowing further customized visualizations of the network and introducing their vertex-centric exploration.


  • Biological network
  • Protein function prediction
  • Information visualization
  • Graph visualization

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Ba, C.T. et al. (2020). A Graphical Tool for the Exploration and Visual Analysis of Biomolecular Networks. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34584-6

  • Online ISBN: 978-3-030-34585-3

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