Skip to main content
  • Research Article
  • Open access
  • Published:

Extraction of Protein Interaction Data: A Comparative Analysis of Methods in Use

Abstract

Several natural language processing tools, both commercial and freely available, are used to extract protein interactions from publications. Methods used by these tools include pattern matching to dynamic programming with individual recall and precision rates. A methodical survey of these tools, keeping in mind the minimum interaction information a researcher would need, in comparison to manual analysis has not been carried out. We compared data generated using some of the selected NLP tools with manually curated protein interaction data (PathArt and IMaps) to comparatively determine the recall and precision rate. The rates were found to be lower than the published scores when a normalized definition for interaction is considered. Each data point captured wrongly or not picked up by the tool was analyzed. Our evaluation brings forth critical failures of NLP tools and provides pointers for the development of an ideal NLP tool.

[123456789101112131415161718]

References

  1. Hunter L, Cohen KB: Biomedical language processing: what's beyond PubMed? Molecular Cell 2006, 21(5):589-594. 10.1016/j.molcel.2006.02.012

    Article  Google Scholar 

  2. Fukuda K, Tamura A, Tsunoda T, Takagi T: Toward information extraction: identifying protein names from biological papers. Pacific Symposium on Biocomputing 1998, 707-718.

    Google Scholar 

  3. Stephens M, Palakal M, Mukhopadhyay S, Raje R, Mostafa J: Detecting gene relations from Medline abstracts. Pacific Symposium on Biocomputing 2001, 483-495.

    Google Scholar 

  4. Sekimizu T, Park HS, Tsujii J: Identifying the interaction between genes and gene products based on frequently seen verbs in medline abstracts. Genome informatics 1998, 9: 62-71.

    Google Scholar 

  5. Novichkova S, Egorov S, Daraselia N: MedScan, a natural language processing engine for Medline abstracts. Bioinformatics 2003, 19(13):1699-1706. 10.1093/bioinformatics/btg207

    Article  Google Scholar 

  6. Yakushiji A, Tateisi Y, Miyao Y, Tsujii J: Event extraction from biomedical papers using a full parser. Pacific Symposium on Biocomputing 2001, 408-419.

    Google Scholar 

  7. Thomas J, Milward D, Ouzounis C, Pulman S, Carroll M: Automatic extraction of protein interactions from scientific abstracts. Pacific Symposium on Biocomputing 2000, 541-552.

    Google Scholar 

  8. Huang M, Zhu X, Hao Y, Payan DG, Qu K, Li M: Discovering patterns to extract protein-protein interactions from full texts. Bioinformatics 2004, 20(18):3604-3612. 10.1093/bioinformatics/bth451

    Article  Google Scholar 

  9. Hu ZZ, Narayanaswamy M, Ravikumar KE, Vijay-Shanker K, Wu CH: Literature mining and database annotation of protein phosphorylation using a rule-based system. Bioinformatics 2005, 21(11):2759-2765. 10.1093/bioinformatics/bti390

    Article  Google Scholar 

  10. Jenssen T-K, Lægreid A, Komorowski J, Hovig E: A literature network of human genes for high-throughput analysis of gene expression. Nature Genetics 2001, 28(1):21-28.

    Google Scholar 

  11. Friedman C, Kra P, Yu H, Krauthammer M, Rzhetsky A: GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics 2001, 17(1):S74-S82. 10.1093/bioinformatics/17.suppl_1.S74

    Article  Google Scholar 

  12. Corney DPA, Buxton BF, Langdon WB, Jones DT: BioRAT: extracting biological information from full-length papers. Bioinformatics 2004, 20(17):3206-3213. 10.1093/bioinformatics/bth386

    Article  Google Scholar 

  13. Ahmed ST, Chidambaram D, Davulcu H, Baral C: IntEx: a syntactic role driven protein-protein interaction extractor for bio-medical text. Association for Computational Linguistics 2005, 54-61.

    Google Scholar 

  14. Eom J, Zhang B: PubMiner: machine learning-based text mining for biomedical information analysis. Genomics & Informatics 2004, 2(2):99-106.

    Google Scholar 

  15. Donaldson I, Martin J, de Bruijn B, et al.: PreBIND and Textomy—mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinformatics 2003, 4(1):11-23. 10.1186/1471-2105-4-11

    Article  Google Scholar 

  16. Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, Mazo I: Extracting human protein interactions from Medline using a full-sentence parser. Bioinformatics 2004, 20(5):604-611. 10.1093/bioinformatics/btg452

    Article  Google Scholar 

  17. Jang H, Lim J, Lim J-H, Park S-J, Lee K-C, Park S-H: Finding the evidence for protein-protein interactions from PubMed abstracts. Bioinformatics 2006, 22(14):e220-e226. 10.1093/bioinformatics/btl203

    Article  Google Scholar 

  18. Corney DPA, Buxton BF, Langdon WB, Jones DT: BioRAT: extracting biological information from full-length papers. Bioinformatics 2004, 20(17):3206-3213. 10.1093/bioinformatics/bth386

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jyothi Devakumar.

Rights and permissions

Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License ( https://creativecommons.org/licenses/by-nc/2.0 ), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Reprints and permissions

About this article

Cite this article

Jose, H., Vadivukarasi, T. & Devakumar, J. Extraction of Protein Interaction Data: A Comparative Analysis of Methods in Use. J Bioinform Sys Biology 2007, 53096 (2007). https://doi.org/10.1155/2007/53096

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/53096

Keywords