Analysis of biological networks has exploded in recent years. A wide variety of technologies have been introduced for mapping networks of gene and protein interactions, including yeast-two-hybrid assays, affinity purification coupled to mass spectrometry, chromatin immunoprecipitation measurements, synthetic-lethal and suppressor networks, expression QTLs, and many others. These technologies have the potential to revolutionize the study of human health, by enabling the construction of large pathway maps which can pinpoint the molecular mechanisms underlying normal and disease states of biological systems. However, the enormous variety and number of new molecular interaction measurements necessitate new algorithms, conceptual frameworks, and software to integrate, query, visualize, and interpret the resulting network data.
The aim of the Resource for Analysis, Visualization, and Integration of Networks (RAVIN) is to provide a freely available, open-source suite of software technology that broadly enables network-based visualization, analysis, and biomedical discovery for NIH-funded researchers. Our overall objectives are to assemble large-scale biological data into models of networks and pathways and to use these networks to better understand how biological systems operate under normal conditions ahd how they fail in disease. These goals will be carried out in four Technology Research and Development projects: A. Network-based biomarkers for disease classification;B. Recognizing trend motifs and dynamics in social networks;C. Network visualization and representation;and D. Predictive modeling and network inference. The centerpiece of our software development and support efforts will be Cytoscape, currently one of the most functional and widely used network visualization and analysis tools. Now in its sixth year of development, Cytoscape is co-developed by a multi-disciplinary team of institutions known as the Cytoscape Consortium, who also comprise the host institutions of our proposed resource.
The RAVIN National Resource will benefit public health by transforming large networks of molecular interactions into knowledge about the mechanisms of disease. It will mine and visualize molecular networks to assemble quantiative and qualitative models of biological pathways and to use these models to predict patient outcomes.
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