citations.bib

line=PMID- 16168087
@Article{Douglas_etal05,
author  ="S. M. Douglas and G. T. Montelione and M. Gerstein",
title   ={PubNet: a flexible system for visualizing literature derived
networks},
journal ={Genome Biol},
volume  ={6},
number  ={9},
pages   ={R80},
month   ={},
year    ={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.},
keywords={*Databases, Bibliographic | Databases, Protein | *Internet | 
*Literature | Medical Subject Headings | Periodicals as Topic | Research | 
Software | 2006/07/21 09:00},
address ={Department of Molecular Biophysics and Biochemistry, Yale
University, New Haven, CT 06520, USA. sdouglas@fas.harvard.edu},
note    ={}
}

line=PMID- 14724320
@Article{Yu_etal04,
author  ="H. Yu and X. Zhu and D. Greenbaum and J. Karro and M. Gerstein",
title   ={TopNet: a tool for comparing biological sub-networks, correlating
protein properties with topological statistics},
journal ={Nucleic Acids Res},
volume  ={32},
number  ={1},
pages   ={328-337},
month   ={},
year    ={2004},
abstract={Biological networks are a topic of great current interest,
particularly with the publication of a number of large genome-wide
interaction datasets. They are globally characterized by a variety of
graph-theoretic statistics, such as the degree distribution, clustering
coefficient, characteristic path length and diameter. Moreover, real
protein networks are quite complex and can often be divided into many
sub-networks through systematic selection of different nodes and edges. For
instance, proteins can be sub-divided by expression level, length, 
amino-acid composition, solubility, secondary structure and function. A 
challenging research question is to compare the topologies of sub- networks, 
looking for global differences associated with different types of proteins. 
TopNet is an automated web tool designed to address this question,
calculating and comparing topological characteristics for different
sub-networks derived from any given protein network. It provides reasonable
solutions to the calculation of network statistics for sub-networks
embedded within a larger network and gives simplified views of a
sub-network of interest, allowing one to navigate through it. After
constructing TopNet, we applied it to the interaction networks and protein
classes currently available for yeast. We were able to find a number of 
potential biological correlations. In particular, we found that soluble
proteins had more interactions than membrane proteins. Moreover, amongst
soluble proteins, those that were highly expressed, had many polar amino
acids, and had many alpha helices, tended to have the most interaction
partners. Interestingly, TopNet also turned up some systematic biases in
the current yeast interaction network: on average, proteins with a known
functional classification had many more interaction partners than those
without. This phenomenon may reflect the incompleteness of the
experimentally determined yeast interaction network.},
keywords={Algorithms | Computational Biology | Databases, Protein | Genomics 
| Internet | Molecular Weight | Protein Binding | Protein Structure,
Secondary | Proteins/*chemistry/genetics/*metabolism | Proteomics | RNA,
Messenger/analysis | Saccharomyces cerevisiae
Proteins/chemistry/genetics/metabolism | *Software | Solubility | 
2004/01/22 05:00},
address ={Department of Molecular Biophysics and Biochemistry, 266 Whitney
Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.},
note    ={}
}