However, the use and adoption of these methods in biomedical research has been slow. Perhaps for this reason the development of methods that infer networks from transcriptomics or other global molecular measurements has recently received much attention ( Marbach et al., 2012 Poultney et al., 2012). In contrast, the experimental measurement of component interactions-important for analyses in a network context-is still far more difficult. The availability of large-scale experimental technologies has enabled the routine measurement of global abundances and states of molecular components in cellular systems. In a case study, we illustrate the transformation of an ARACNE implementation into a Cytoscape app.Īvailability and implementation: Cyni, its apps, user guides, documentation and sample code are available from the Cytoscape App Store Merely placing the resulting app in the Cytoscape App Store makes the method accessible to a worldwide community of biomedical researchers by mouse click. Cyni allows the rapid transformation of Java-based network inference prototypes into apps of the popular open-source Cytoscape network analysis and visualization ecosystem. Here, we present Cyni, an open-source ‘fill-in-the-algorithm’ framework that provides common network inference functionality and user interface elements. Motivation: Research on methods for the inference of networks from biological data is making significant advances, but the adoption of network inference in biomedical research practice is lagging behind.
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