  Gene/protein interactions provide critical information for a thorough
understanding of cellular processes. Recently, considerable interest and effort
has been focused on the construction and analysis of genome-wide gene networks.
The large body of biomedical literature is an important source of gene/protein
interaction information. Recent advances in text mining tools have made it
possible to automatically extract such documented interactions from free-text
literature. In this paper, we propose a comprehensive framework for
constructing and analyzing large-scale gene functional networks based on the
gene/protein interactions extracted from biomedical literature repositories
using text mining tools. Our proposed framework consists of analyses of the
network topology, network topology-gene function relationship, and temporal
network evolution to distill valuable information embedded in the gene
functional interactions in literature. We demonstrate the application of the
proposed framework using a testbed of P53-related PubMed abstracts, which shows
that literature-based P53 networks exhibit small-world and scale-free
properties. We also found that high degree genes in the literature-based
networks have a high probability of appearing in the manually curated database
and genes in the same pathway tend to form local clusters in our
literature-based networks. Temporal analysis showed that genes interacting with
many other genes tend to be involved in a large number of newly discovered
interactions.
