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PubNet: a flexible system for visualizing literature derived networks

Shawn M Douglas1, Gaetano T Montelione2 and Mark Gerstein13*

Author Affiliations

1 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA

2 Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, Rutgers University and Robert Wood Johnson Medical School, Piscataway, NJ 08854, USA

3 Department of Computer Science, Yale University, New Haven, CT 06520, USA

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Genome Biology 2005, 6:R80  doi:10.1186/gb-2005-6-9-r80

Published: 16 August 2005

Abstract

We have developed PubNet, a web-based tool that extracts several types of relationships returned by PubMed queries and maps them into networks, allowing for graphical visualization, textual navigation, and topological analysis. PubNet supports the creation of complex networks derived from the contents of individual citations, such as genes, proteins, Protein Data Bank (PDB) IDs, Medical Subject Headings (MeSH) terms, and authors. This feature allows one to, for example, examine a literature derived network of genes based on functional similarity.