ProteoLens in 10 min: https://github.com/aimed-lab/ProteoLens/blob/main/docs/ProteoLensin10minutes.pdf
Contact: jakechen@uab.edu
Huan, T., Sivachenko, A. Y., Harrison, S. H., & Chen, J. Y. (2008). ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining. BMC bioinformatics, 9 Suppl 9(Suppl 9), S5. https://doi.org/10.1186/1471-2105-9-S9-S5
We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network.