Skip to main content

Advertisement

Log in

Text mining biomedical literature for constructing gene regulatory networks

  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

In this paper, we present the framework of a Gene Regulatory Networks System: GRNS. The goals of GRNS include automatically mining biomedical literature to extract gene regulatory information (strain number, genotype, gene regulatory relation, and phenotype), automatically constructing gene regulatory networks based on extracted information and integrating biomedical knowledge into the regulatory networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Abney, S. 1996. Statistical Methods and Linguistics. In: The Balancing Act: Combining Symbolic and Statistical Approaches to Language. The MIT Press, 1–26.

  2. Afantenos, S., Karkaletsis, V., Stamatopoulos, P. 2005. Summarization from medical documents: a survey. Artificial Intelligence in Medicine 33, 157–177.

    Article  PubMed  Google Scholar 

  3. Blaschke, C., Andrade, M.A., Ouzounis, C., Valencia, A. 1999. Automatic extraction of biological information from scientific text: protein-protein interactions. In: Proceedings International Conference on Intelligent Systems in Molecular Biology 7, 60–67.

    Google Scholar 

  4. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O’Donovan, C., Phan, I. 2003. The SWISS-PROT protein knowledgebase and its supplement TREMBL. Nucleic Acids Research 31, 365–370.

    Article  CAS  PubMed  Google Scholar 

  5. Brill, E. 1995. Transformation-based error-driven learning and natural language pro-pessing: A case study in part-of-speech tagging. Computational Linguistics 21, 543–566.

    Google Scholar 

  6. Chun, H., Tsuruoka, Y., Kim, J., Shiba, R., Nagata, N., Hishiki, T., Tsujie, J. 2005. Etraction of Gene-Disease relations from medline using domain dictionaries and maching learning. In: Proceedings of Pacific Symppsium Biocomputing 11, 4–15.

    Google Scholar 

  7. Cohen, A.M., Hersh, W.R. 2005. A survey of current work in biomedical text mining. Briefings in Bioinformatics 6, 57–71.

    Article  CAS  PubMed  Google Scholar 

  8. Cohen, K., Hunter, L. 2004. Natural language processing and systems biology. In: Dubitzky and Pereira (eds), Artificial intelligence methods and tools for systems biology, Springer Verlag, 147–173.

  9. Friedman, C., Kra, P., Yu, H., Krauthammer, M., Rzhetsky, A. 2001. GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics 17 (Suppl 1), S74–82.

    PubMed  Google Scholar 

  10. Goodman, L., Lory, S. 2004. Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling. Current Opinions on Microbiology 7, 39–44.

    Article  CAS  Google Scholar 

  11. Hirschman, L., Park, J.C., Tsujii, J., Wong, L., Wu, C.H. 2003. Accomplishments and challenges in literature data mining for biology. Bioinformatics 18, 1553–1561.

    Article  Google Scholar 

  12. Hu, Z.Z., Narayanaswamy, M., Ravikumar, K.E., Vijay-Shanker, K., Wu, C.H. 2005. Literature mining and database annotation of protein phosphorylation using a rule-based system. Bioinformatics 21, 2759–2765.

    Article  CAS  PubMed  Google Scholar 

  13. Ling, X., Jing, J., He, X., Mei, Q., Zhai, C., Schatz, B. 2006. Automatically generating gene summaries from biomedical literature. In: Proceeding of Pacific Symposium Biocomputing, 11, 41–50.

    Google Scholar 

  14. Liu, H., Friedman, C. 2003. Mining terminological knowledge in large biomedical corpora. In: Proceeding of Pacific Symposium Biocomputing 8, 415–426.

    Google Scholar 

  15. Marcotte, E.M., Xenarios, I., Eisenberg, D. 2001. Mining literature for protein-protein interactions. Bioinformatics 17, 359–363.

    Article  CAS  PubMed  Google Scholar 

  16. McDonald, D.M., Chen, H., Su, H., Marshall, B.B. 2004. Extracting gene pathway relations using a hybrid grammar: the Arizona Relation Parser. Bioinformatics 20, 3370–3378.

    Article  CAS  PubMed  Google Scholar 

  17. Nenadic, G., Spasic, I., Ananiadou, S. 2003. Terminology-driven mining of biomedical literature. Bioinformatics 19, 938–943.

    Article  CAS  PubMed  Google Scholar 

  18. Saric, J., Jensen, L.J., Ouzounova, R., Rojas, I., Bork, P. 2005. Extraction of regulatory gene / protein networks from Medline. Bioinformatics 22, 654–650.

    Google Scholar 

  19. Serino, L., Reimmann, C., Visca, P., Beyeler, M., Chiesa, V.D., Haas, D. 1997. Biosynthesis of pyochelin and dihydroaeruginoic acid requires the iron-regulated pchDCBA operon in Pseudomonas aeruginosa. Journal of Bacteriology 179, 248–257.

    CAS  PubMed  Google Scholar 

  20. Shatkay, H., Feldman, R. 2003. Mining the biomedical literature in the genomic era: an overview. Journal of Computational Biolology 10, 821–855.

    Article  CAS  Google Scholar 

  21. Shiwani, A.K., Ritchings, B.W., Almina, E.C., Lory, S., Ramphal, R. 1997. A Transcriptional Activator, FleQ, Regulates Mucin Adhesion and Flagellar Gene Expression in Pseudomonas aeruginosa in a Cascade Manner. Journal of Bacteriology 179, 5574–5581.

    Google Scholar 

  22. Stover, C.K., Pham, X.Q., Erwin, A.L., Mizoguchi, P., Warrener, M.J., Hickey, F.S., Brinkman, S.D., Hufnagle, W.O., Kowalik, D.J., Lagrou, M., Garber, R.L., Goltry, L., Tolentino, E., Westbrock-Wadman, S., Yuan, Y., Brody, L.L., Coulter, S.N., Folger, K.R., Kas, A., Larbig, K., Lim, R., Smith, K., Spencer, D., Wong, G.K., Wu, Z., Paulsen, I.T., Reizer, J., Saier, M.H., Hancock, R.E., Lory, S., Olson, M.V. 2000. Complete genome sequence of Pseudomonas aeruginosa PA01, an opportunistic pathogen. Nature 406, 959–964.

    Article  CAS  PubMed  Google Scholar 

  23. Woods, D.E. 2004. Comparative genomic analysis of Pseudomonas aeruginosa virulence. Trends Microbiology 12, 437–439.

    Article  CAS  Google Scholar 

  24. Wu, W., Song, Y., Jin S., Chen, S. 2005. An Interactive Map of Regulatory Networks of Pseudomonas aeruginosa Genome. In: Proceedings of First RECOMBS Satellite Workshop on Systems Biology, 1–10.

  25. Yakushiji, A., Tateisi, Y., Miyao, Y., Tsujii, J. 2001. Event extraction from biomedical papers using a full parser. In: Proceedings of Pacific Symposium Biocomputing, 408–419.

  26. Yandell, M.D., Majoros, W.H. 2003. Genomics and natural language processing. Nature Reviews Genetics 3, 601–610.

    Google Scholar 

  27. Yuan, X., Hu, Z.Z., Wu, H.T., Torii, M., Narayanaswamy, M., Ravikumar, K.E., Vijay-Shanker, K., Wu, C.H. 2006. An online literature mining tool for protein phosphorylation. Bioinformatics 22, 1668–1669.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Su-Shing Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, YL., Chen, SS. Text mining biomedical literature for constructing gene regulatory networks. Interdiscip Sci Comput Life Sci 1, 179–186 (2009). https://doi.org/10.1007/s12539-009-0028-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-009-0028-7

Key words

Navigation