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Molecular Biology

, Volume 44, Issue 1, pp 140–149 | Cite as

Construction of protein semantic networks using PubMed/MEDLINE

  • E. A. PonomarenkoEmail author
  • A. V. Lisitsa
  • E. V. Il’gisonis
  • A. I. Archakov
Bioinformatics

Abstract

A method for constructing protein semantic networks using MEDLINE abstracts is proposed. The publications retrieved by the context search for protein names (relevant) and the related publications were used. The proposed method is based on estimation of semantic connectivity between proteins. The connectivity score was calculated as a function of the number of relevant or related papers found for a pair of proteins. This score was used to construct a semantic network for 150 human proteins belonging to five different metabolic pathways. Analysis of the network demonstrated that the proteins involved in associated molecular processes formed the subgraphs with a high edge density.

Key words

semantic network metabolic pathway relevant papers 

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References

  1. 1.
    Stapley B. J., Benoit G. 2000. Biobibliometrics: Information retrieval and visualization from co-occurrences of gene names in Medline abstracts. Pac. Symp. Biocomput. 529–540.Google Scholar
  2. 2.
    Harris M. A., Clark J., Ireland A., et al. 2004. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32, D258–D261.CrossRefPubMedGoogle Scholar
  3. 3.
    Beissbarth T. 2006. Interpreting experimental results using gene ontologies. Meth. Enzymology. 411, 340–352.CrossRefGoogle Scholar
  4. 4.
    Zheng B., Lu X. 2007. Novel metrics for evaluating the functional coherence of protein groups via protein semantic network. Genome Biol. 8, R153.CrossRefPubMedGoogle Scholar
  5. 5.
    Anan’ko E.A., Likhoswvay V.A., Kolpakov F.A., Podkolodnyi N.L., Ratushnyi A.V., Ignat’eva E.V., Podkolodnaya O.A., Stepanenko I.L., Kolchanov N.A. 2000. GeneNet electronic library: Description and modeling of animal and plant gene networks. Proc. 2nd All-Russia Conf. “Electronic Libraries: Promising Methods and Technologies, Electronic Collections” pp. 66–72.Google Scholar
  6. 6.
    Homayouni R., Heinrich K., Wei L., Berry M. W. 2005. Gene clustering by latent semantic indexing of MED-LINE abstracts. Bioinformatics. 21, 104–115.CrossRefPubMedGoogle Scholar
  7. 7.
    Bundschus M., Dejori M., Stetter M., Tresp V., Kriegel H.P. 2008. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics. 9, 207.CrossRefPubMedGoogle Scholar
  8. 8.
    Jensen L.J., Kuhn M., Stark M., et al. 2009. STRING 8: A global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412–D416.CrossRefPubMedGoogle Scholar
  9. 9.
    Kanehisa M., Araki M., Goto S., Hattori M., Hirakawa M., Itoh M., Katayama T., Kawashima S., Okuda S., Tokimatsu T., Yamanishi Y. 2008. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484.CrossRefPubMedGoogle Scholar
  10. 10.
    Rogers D.J., Tanimoto T.T. 1960. A Computer Program for Classifying Plants. Science. 132, 1115–1118.CrossRefPubMedGoogle Scholar
  11. 11.
    Lin J., Wilbur W.J. 2007. PubMed related articles: A probabilistic topic-based model for content similarity. BMC Bioinformatics. 8, 423.CrossRefPubMedGoogle Scholar
  12. 12.
    Wang Y., Marsden P.A. 1995. Nitric oxide synthases: Gene structure and regulation. Adv. Pharmacology. 34, 71–90.CrossRefGoogle Scholar
  13. 13.
    Nadanaka S., Kitagawa H. 2008. Heparan sulphate biosynthesis and disease. J. Biochemistry. 144, 7–14.CrossRefGoogle Scholar
  14. 14.
    Homayouni R., Heinrich K., Wei L., Berry M.W. 2005. Gene clustering by latent semantic indexing of MED-LINE abstracts. Bioinformatics. 21, 104–115.CrossRefPubMedGoogle Scholar
  15. 15.
    Omenn G., States D.J., Adamski M., et al. 2005. Over-view of the HUPO Plasma Proteome Project. Proteomics. 5, 3226–3245.CrossRefPubMedGoogle Scholar
  16. 16.
    Raychaudhuri S., Altman R.B. 2003. A literature-based method for assessing the functional coherence of a gene group. Bioinformatics. 19, 396–401.CrossRefPubMedGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2010

Authors and Affiliations

  • E. A. Ponomarenko
    • 1
    Email author
  • A. V. Lisitsa
    • 1
  • E. V. Il’gisonis
    • 2
  • A. I. Archakov
    • 1
  1. 1.Orekhovich Institute of Biomedical ChemistryRussian Academy of Medical SciencesMoscowRussia
  2. 2.KuB Ltd.MoscowRussia

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