BioGraphE: high-performance bionetwork analysis using the Biological Graph Environment

Background: Graphs and networks are common analysis representations for biological systems. Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great potential as analysis techniques for newly available data in biology. Yet, as the amount of genomic and bionetwork information rapidly grows, scientists need advanced new computational strategies and tools for dealing with the complexities of the bionetwork analysis and the volume of the data. Results: We introduce a computational framework for graph analysis called the Biological Graph Environment (BioGraphE), which provides a general, scalable integration platform for connecting graph problems in biology to optimized computational solvers and high-performance systems. This framework enables biology researchers and computational scientists to identify and deploy network analysis applications and to easily connect them to efficient and powerful computational software and hardware that are specifical...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: George Chin Jr., Daniel G. Chavarría-Miranda, Grant C. Nakamura, Heidi J. Sofia
• Keywords: Analysis, BMCBI 2008, Computational Solver, Graph Problems