Skip to content


  • Research Article
  • Open Access

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

EURASIP Journal on Bioinformatics and Systems Biology20072007:53096

  • Received: 31 March 2007
  • Accepted: 8 October 2007
  • Published:


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.


  • Comparative Analysis
  • Protein Interaction
  • Natural Language
  • Dynamic Programming
  • System Biology


Authors’ Affiliations

Jubilant Biosys Ltd., #96, Industrial Suburb, 2nd Stage, Yeshwanthpur, Bangalore, 560 022, India


  1. Hunter L, Cohen KB: Biomedical language processing: what's beyond PubMed? Molecular Cell 2006, 21(5):589-594. 10.1016/j.molcel.2006.02.012View ArticleGoogle 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/btg207View ArticleGoogle 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/bth451View ArticleGoogle 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/bti390View ArticleGoogle 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.S74View ArticleGoogle 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/bth386View ArticleGoogle 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. 10.1186/1471-2105-4-11View ArticleGoogle 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/btg452View ArticleGoogle 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/btl203View ArticleGoogle 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/bth386View ArticleGoogle Scholar


© Hena Jose et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.