Large amounts of various biological networks exist for representing different types of interaction data, such as genetic, metabolic, gene regulatory, and protein-protein relationships. Recent approaches on biological network study are… Click to show full abstract
Large amounts of various biological networks exist for representing different types of interaction data, such as genetic, metabolic, gene regulatory, and protein-protein relationships. Recent approaches on biological network study are based on different mathematical concepts. It is necessary to construct a uniform framework to judge the functionality of biological networks. We recently introduced a knowledge-based computational framework that reliably characterized biological networks in system level. The method worked by making systematic comparisons to a set of well-studied “basic networks,” measuring both the functional and topological similarities. A biological network could be characterized as a spectrum-like vector consisting of similarities to basic networks. Here, to facilitate the application, development, and adoption of this framework, we present an R package called NFP. This package extends our previous pipeline, offering a powerful set of functions for Network Fingerprint analysis. The software shows great potential in biological network study. The open source NFP R package is freely available under the GNU General Public License v2.0 at CRAN along with the vignette.
               
Click one of the above tabs to view related content.