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A literature network of human genes for high-throughput analysis of gene expression

Nature Genetics volume 28, pages 21–28 (2001)Cite this article

Abstract

We have carried out automated extraction of explicit and implicit biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for 13,712 named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records. The associations between genes have been annotated by linking genes to terms from the medical subject heading (MeSH) index and terms from the gene ontology (GO) database. The extracted database and accompanying web tools for gene-expression analysis have collectively been named 'PubGene'. We validated the extracted networks by three large-scale experiments showing that co-occurrence reflects biologically meaningful relationships, thus providing an approach to extract and structure known biology. We validated the applicability of the tools by analyzing two publicly available microarray data sets.

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Figure 1: Gene-to-article and gene-to-gene distributions.
Figure 2: Literature networks of genes found relevant in gene expression data analysis.

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Acknowledgements

We thank the National Library of Medicine for access to MEDLINE; D. Tjeldvoll for help with installation and use of the Graphviz software, and contributions to programming on early versions of the web-interface; H.-C. Aasheim and Ø. Fodstad for discussions; and S. Bade, W.P. Kuo, S. Vinterbo and D. Warren for comments on the manuscript. This work was supported in part by grants from the Norwegian Cancer Society. T.K.J. was supported by grant 134422/410 from the Norwegian Research Council.

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Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

    Tor-Kristian Jenssen & Jan Komorowski

  2. Department of Physiology and Biomedical Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Astrid Lægreid

  3. Department of Tumour Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo, Norway

    Eivind Hovig

  4. Department of Computer and Information Science, Linköping University, Sweden

    Jan Komorowski

Authors
  1. Tor-Kristian Jenssen
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  2. Astrid Lægreid
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  3. Jan Komorowski
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  4. Eivind Hovig
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Correspondence to Eivind Hovig.

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Jenssen, TK., Lægreid, A., Komorowski, J. et al. A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 28, 21–28 (2001). https://doi.org/10.1038/ng0501-21

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