Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2013, Article ID 410294, 9 pages
http://dx.doi.org/10.1155/2013/410294
Research Article

Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature

1Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Boadilla del Monte, 28660 Madrid, Spain
2Biomedical Informatics Group, Facultad de Informática, Universidad Politécnica de Madrid, Boadilla del Monte, 28660 Madrid, Spain

Received 8 May 2012; Revised 3 July 2012; Accepted 10 July 2012

Academic Editor: Raffaele Calogero

Copyright © 2013 Miguel García-Remesal et al. 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.

Linked References

  1. V. Maojo, F. Martin-Sanchez, C. Kulikowski, A. Rodriguez-Paton, and M. Fritts, “Nanoinformatics and DNA-based computing: catalyzing nanomedicine,” Pediatric Research, vol. 67, no. 5, pp. 481–489, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. V. Maojo, M. García-Remesal, D. de la Iglesia, and J. Crespo, “Nanoinformatics: developing advanced informatics applications for Nanomedicine,” in Intracellular Drug Delivery: Fundamentals and Applications, A. Prokop, Ed., vol. 5, pp. 847–860, 2011. View at Google Scholar
  3. ACTION-Grid consortium, The ACTION-Grid White Paper on Nanoinformatics, http://www.action-grid.eu/index.php?url=whitepaper, 2010.
  4. D. de la Iglesia, V. Maojo, S. Chiesa et al., “International efforts in nanoinformatics research applied to nanomedicine.,” Methods of Information in Medicine, vol. 50, no. 1, pp. 84–95, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Bolhassani, S. Safaiyan, and S. Rafati, “Improvement of different vaccine delivery systems for cancer therapy,” Molecular Cancer, vol. 10, p. 3, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Kosuge, S. P. Sherlock, T. Kitagawa et al., “FeCo/graphite nanocrystals for multi-modality imaging of experimental vascular inflammation,” PLoS ONE, vol. 6, no. 1, Article ID e14523, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. A. S. Thakor, R. Paulmurugan, P. Kempen et al., “Oxidative stress mediates the effects of raman-active gold nanoparticles in human cells,” Small, vol. 7, no. 1, pp. 126–136, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. R. A. Freitas, Nanomedicine, Volume I: Basic Capabilities, Landes Bioscience, Georgetown, Tex, USA, 1999.
  9. R. A. Freitas, Nanomedicine, Volume IIA: Biocompatibility, Landes Bioscience, Georgetown, Tex, USA, 2003.
  10. M. Li, L. Zhu, and D. Lin, “Toxicity of ZnO nanoparticles to Escherichia coli: mechanism and the influence of medium components,” Environmental Science and Technology, vol. 45, no. 5, pp. 1977–1983, 2011. View at Publisher · View at Google Scholar
  11. R. Hu, L. Zheng, T. Zhang et al., “Mechanism of inflammatory responses in brain and impairment of spatial memory of mice caused by titanium dioxide nanoparticles,” Journal of Hazardous Materials, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Chen, X. Dong, Y. Xin, and M. Zhao, “Effects of titanium dioxide nano-particles on growth and some histological parameters of zebrafish (Danio rerio) after a long-term exposure,” Aquatic Toxicology, vol. 101, no. 3-4, pp. 493–499, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Marushima, K. Suzuki, Y. Nagasaki et al., “Newly synthesized radical-containing nanoparticles enhance neuroprotection after cerebral ischemia-reperfusion injury,” Neurosurgery, vol. 68, no. 5, pp. 1418–1425, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Sharma, A. Tandon, J. C. K. Tovey et al., “Polyethylenimine-conjugated gold nanoparticles: gene transfer potential and low toxicity in the cornea,” Nanomedicine, vol. 7, no. 4, pp. 505–513, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. M. García-Remesal, V. Maojo, J. Crespo, and H. Billhardt, “Logical schema acquisition from text-based sources for structured and non-structured biomedical sources integration,” AMIA Annual Symposium Proceedings, pp. 259–263, 2007. View at Google Scholar · View at Scopus
  16. M. García-Remesal, A. Cuevas, V. López-Alonso et al., “A method for automatically extracting infectious disease-related primers and probes from the literature,” BMC Bioinformatics, vol. 11, p. 410, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. M. García-Remesal, A. Cuevas, D. Pérez-Rey et al., “PubDNA Finder: a web database linking full-text articles to sequences of nucleic acids,” Bioinformatics, vol. 26, no. 21, Article ID btq520, pp. 2801–2802, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M. García-Remesal, V. Maojo, H. Billhardt, and J. Crespo, “Integration of relational and textual biomedical sources: a pilot experiment using a semi-automated method for logical schema acquisition,” Methods of Information in Medicine, vol. 49, no. 4, pp. 337–348, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. J. T. Chang, H. Schütze, and R. B. Altman, “GAPSCORE: finding gene and protein names one word at a time,” Bioinformatics, vol. 20, no. 2, pp. 216–225, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Mika and B. Rost, “NLProt: extracting protein names and sequences from papers,” Nucleic Acids Research, vol. 32, supplement 2, pp. W634–W637, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. B. Settles, “ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text,” Bioinformatics, vol. 21, no. 14, pp. 3191–3192, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. L. Tanabe, N. Xie, L. H. Thom, W. Matten, and W. J. Wilbur, “GENETAG: a tagged corpus for gene/protein named entity recognition,” BMC Bioinformatics, vol. 6, supplement 1, p. S3, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Torii, Z. Hu, C. H. Wu, and H. Liu, “BioTagger-GM: a gene/protein name recognition system,” Journal of the American Medical Informatics Association, vol. 16, no. 2, pp. 247–255, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. Ö. Uzuner, I. Solti, and E. Cadag, “Extracting medication information from clinical text,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 514–518, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. J. D. Wren, W. H. Hildebrand, S. Chandrasekaran, and U. Melcher, “Markov model recognition and classification of DNA/protein sequences within large text databases,” Bioinformatics, vol. 21, no. 21, pp. 4046–4053, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Aerts, M. Haeussler, S. van Vooren et al., “Text-mining assisted regulatory annotation,” Genome Biology, vol. 9, no. 2, p. R31, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. J. C. Park and J. Kim, “Named entity recognition,” in Text Mining for Biology and Biomedicine, S. Ananiadou and J. McNaught, Eds., pp. 121–142, Artech House, Norwood, Mass, USA, 2006. View at Google Scholar
  28. T. K. Jenssen, A. Lægreid, J. Komorowski, and E. Hovig, “A literature network of human genes for high-throughput analysis of gene expression,” Nature Genetics, vol. 28, no. 1, pp. 21–28, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. T. Ono, H. Hishigaki, A. Tanigami, and T. Takagi, “Automated extraction of information on protein-protein interactions from the biological literature,” Bioinformatics, vol. 17, no. 2, pp. 155–161, 2001. View at Google Scholar · View at Scopus
  30. T. Hamon and N. Grabar, “Linguistic approach for identification of medication names and related information in clinical narratives,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 549–554, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. R. Farkas, G. Szarvas, I. Hegedus et al., “Semi-automated Construction of Decision Rules to Predict Morbidities from Clinical Texts,” Journal of the American Medical Informatics Association, vol. 16, no. 4, pp. 601–605, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. A. R. Aronson, “Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program,” Proceedings of the Annual Symposium Proceedings (AMIA '01), pp. 17–21, 2001. View at Google Scholar · View at Scopus
  33. J. G. Mork, O. Bodenreider, D. Demner-Fushman et al., “Extracting Rx information from clinical narrative,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 536–539, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Krauthammer, A. Rzhetsky, P. Morozov, and C. Friedman, “Using BLAST for identifying gene and protein names in journal articles,” Gene, vol. 259, no. 1-2, pp. 245–252, 2000. View at Publisher · View at Google Scholar · View at Scopus
  35. S. F. Altschul, T. L. Madden, A. A. Schäffer et al., “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs,” Nucleic Acids Research, vol. 25, no. 17, pp. 3389–3402, 1997. View at Publisher · View at Google Scholar · View at Scopus
  36. K. Fukuda, A. Tamura, T. Tsunoda, and T. Takagi, “Toward information extraction: identifying protein names from biological papers.,” Pacific Symposium on Biocomputing, pp. 707–718, 1998. View at Google Scholar · View at Scopus
  37. D. Proux, F. Rechenmann, L. Julliard, V. V. Pillet, and B. Jacq, “Detecting gene symbols and names in biological texts: a first step toward pertinent information extraction,” in Proceedings of the Workshop on Genome Informatics, vol. 9, pp. 72–80, 1998.
  38. R. Gaizauskas, G. Demetriou, and K. Humphreys, “Term recognition and classification in biological science journal articles,” in Proceedings of the Workshop on Computational Terminology for Medical and Biological Applications, pp. 37–44, 2000.
  39. L. C. Childs, R. Enelow, L. Simonsen, N. H. Heintzelman, K. M. Kowalski, and R. J. Taylor, “Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data,” Journal of the American Medical Informatics Association, vol. 16, no. 4, pp. 571–575, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. N. K. Mishra, D. M. Cummo, J. J. Arnzen, and J. Bonander, “A Rule-based Approach for Identifying Obesity and Its Comorbidities in Medical Discharge Summaries,” Journal of the American Medical Informatics Association, vol. 16, no. 4, pp. 576–579, 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. I. Spasić, F. Sarafraz, J. A. Keane, and G. Nenadić, “Medication information extraction with linguistic pattern matching and semantic rules,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 532–535, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. H. Yang, “Automatic extraction of medication information from medical discharge summaries,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 545–548, 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Lafferty, A. McCallum, and F. Pereira, “Conditional random fields: probabilistic models for segmenting and labeling sequence data,” in Proceedings of the 18th International Conference on Machine Learning, pp. 282–289, Morgan Kaufmann, Williamstown, Mass, USA, 2001.
  44. Z. Li, F. Liu, L. Antieau, Y. Cao, and H. Yu, “Lancet: a high precision medication event extraction system for clinical text,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 563–567, 2010. View at Publisher · View at Google Scholar
  45. J. Patrick and M. Li, “High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 524–527, 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. D. Tikk and I. Solt, “Improving textual medication extraction using combined conditional random fields and rule-based systems,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 540–544, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. N. Collier, C. Nobata, and J. Tsujii, “Extracting the names of genes and gene products with a hidden Markov model,” in Proceedings of the 18th Conference on Computational Linguistics, vol. 1, pp. 201–207, 2000.
  48. G. Zhou and J. Su, “Named entity recognition using an HMM-based chunk tagger,” in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 473–480, 2002.
  49. A. A. Morgan, L. Hirschman, M. Colosimo, A. S. Yeh, and J. B. Colombe, “Gene name identification and normalization using a model organism database,” Journal of Biomedical Informatics, vol. 37, no. 6, pp. 396–410, 2004. View at Publisher · View at Google Scholar · View at Scopus
  50. I. Solt, D. Tikk, V. Gál, and Z. T. Kardkovács, “Semantic classification of diseases in discharge summaries using a context-aware rule-based classifier,” Journal of the American Medical Informatics Association, vol. 16, no. 4, pp. 580–584, 2009. View at Publisher · View at Google Scholar · View at Scopus
  51. T. Ohta, Y. Tateisi, J. D. Kim, and J. Tsujii, “The GENIA Corpus: an annotated corpus in molecular biology domain,” in Proceedings of the 2nd International Conference on Human Language Technology Research, pp. 82–86, 2002.
  52. A. Yeh, A. Morgan, M. Colosimo, and L. Hirschman, “BioCreAtIvE task 1A: gene mention finding evaluation,” BMC Bioinformatics, vol. 6, supplement 1, p. S2, 2005. View at Publisher · View at Google Scholar · View at Scopus
  53. M. Gerner, G. Nenadic, and C. M. Bergman, “LINNAEUS: a species name identification system for biomedical literature,” BMC Bioinformatics, vol. 11, p. 85, 2010. View at Publisher · View at Google Scholar · View at Scopus
  54. Ö. Uzuner, I. Solti, F. Xia, and E. Cadag, “Community annotation experiment for ground truth generation for the i2b2 medication challenge,” Journal of the American Medical Informatics Association, vol. 17, no. 5, pp. 519–523, 2010. View at Publisher · View at Google Scholar · View at Scopus
  55. Ö. Uzuner, “Recognizing Obesity and Comorbidities in Sparse Data,” Journal of the American Medical Informatics Association, vol. 16, no. 4, pp. 561–570, 2009. View at Publisher · View at Google Scholar · View at Scopus
  56. G. K. Savova, W. W. Chapman, J. Zheng, and R. S. Crowley, “Anaphoric relations in the clinical narrative: corpus creation,” Journal of the American Medical Informatics Association, vol. 18, no. 4, pp. 459–465, 2011. View at Publisher · View at Google Scholar · View at Scopus
  57. K. S. Jones, “A statistical interpretation of term specificity and its application in retrieval,” Journal of Documentation, vol. 60, no. 5, pp. 493–502, 2004. View at Publisher · View at Google Scholar
  58. C. Rosse and J. L. V. Mejino Jr, “A reference ontology for biomedical informatics: the foundational model of anatomy,” Journal of Biomedical Informatics, vol. 36, no. 6, pp. 478–500, 2003. View at Publisher · View at Google Scholar · View at Scopus
  59. D. G. Thomas, R. V. Pappu, and N. A. Baker, “NanoParticle ontology for cancer nanotechnology research,” Journal of Biomedical Informatics, vol. 44, no. 1, pp. 59–74, 2011. View at Publisher · View at Google Scholar · View at Scopus
  60. Y. Garten and R. B. Altman, “Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text,” BMC bioinformatics, vol. 10, supplement 2, p. S6, 2009. View at Google Scholar · View at Scopus
  61. Gene Ontology Consortium, “The Gene Ontology in 2010: extensions and refinements,” Nucleic Acids Research, vol. 38, pp. D331–D335, 2010. View at Google Scholar