Visualization and exploration of biological networks at such scale are a computationally challenging task and many efforts in this direction have failed over the years. As such networks are characterized by different properties and topologies, graph theory comes to play a very important role by providing ways to efficiently store, analyze, and subsequently visualize them. Nowadays, biological repositories expand every day by hosting various entities such as proteins, genes, drugs, chemicals, ontologies, functions, articles, and the interactions between them, often leading to large-scale networks of thousands or even millions of nodes and connections. Health and natural sciences have become protagonists in the big-data world as high-throughput advances continuously contribute to the exponential growth of data volumes. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth.

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them.

Gephi vs cytoscape