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

• URL: http://arxiv.org/abs/2204.10476v1
• Date: Fri, 22 Apr 2022 03:04:19 GMT
• Title: Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions
• Authors: Xin Li, Hsinchun Chen, Zan Huang, Hua Su, Jesse D. Martinez
• Abstract summary: 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. 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.
• Score: 7.361249273831739
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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