PubNet: a flexible system for visualizing literature derived networks

Shawn M Douglas¹ , Gaetano T Montelione² and Mark Gerstein¹^,3

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

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

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

author email corresponding author email

Genome Biology 2005, 6:R80doi:10.1186/gb-2005-6-9-r80


Published:	16 August 2005

Subject areas: Bioinformatics

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.