Global mapping of gene/protein interactions in PubMed abstracts: a framework and an experiment with P53 interactions.
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 the literature. We demonstrate the application of the proposed framework using a testbed of P53-related PubMed abstracts, which shows that the 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.
Li
Xin
X
Artificial Intelligence Lab, Department of Management Information Systems, The University of Arizona, McClelland Hall, Tucson, AZ 85721-0108, USA. xinli@email.arizona.edu <xinli@email.arizona.edu>
Chen
Hsinchun
H
Huang
Zan
Z
Su
Hua
H
Martinez
Jesse D
JD
D000043:Abstracting and Indexing / Q000379:methods*; D002874:Chromosome Mapping / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D025941:Protein Interaction Mapping / Q000379:methods*; D039781:PubMed; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*; D016159:Tumor Suppressor Protein p53 / Q000378:metabolism*
D014157:Transcription Factors; D016159:Tumor Suppressor Protein p53