Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions

TL;DR

This paper introduces a framework for constructing and analyzing large-scale gene/protein interaction networks from biomedical literature, demonstrating its application on P53 interactions and revealing key network properties.

Contribution

It presents a comprehensive framework for extracting and analyzing gene networks from literature, including topology, function, and temporal evolution, with a case study on P53.

Findings

Literature-based P53 networks exhibit small-world and scale-free properties.

High degree genes in literature networks often appear in curated databases.

Genes with many interactions tend to be involved in new discoveries.

Abstract

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.