About this Journal Submit a Manuscript Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 132724, 6 pages
http://dx.doi.org/10.1155/2013/132724
Research Article

Prediction of Drugs Target Groups Based on ChEBI Ontology

1Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
3Institute of Systems Biology, Shanghai University, Shanghai 200444, China
4Beijing Genomics Institute, Shenzhen Beishan Industrial Zone, Shenzhen 518083, China
5CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Received 15 September 2013; Accepted 28 October 2013

Academic Editor: Tao Huang

Copyright © 2013 Yu-Fei Gao 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. J. Knowles and G. Gromo, “Target selection in drug discovery,” Nature Reviews Drug Discovery, vol. 2, no. 1, pp. 63–69, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. A. C. Cheng, R. G. Coleman, K. T. Smyth et al., “Structure-based maximal affinity model predicts small-molecule druggability,” Nature Biotechnology, vol. 25, no. 1, pp. 71–75, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Rarey, B. Kramer, T. Lengauer, and G. Klebe, “A fast flexible docking method using an incremental construction algorithm,” Journal of Molecular Biology, vol. 261, no. 3, pp. 470–489, 1996. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Zhu, Y. Okuno, G. Tsujimoto, and H. Mamitsuka, “A probabilistic model for mining implicit “chemical compound-gene” relations from literature,” Bioinformatics, vol. 21, no. 2, pp. ii245–ii251, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, “Prediction of drug-target interaction networks from the integration of chemical and genomic spaces,” Bioinformatics, vol. 24, no. 13, pp. i232–i240, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. Z. He, J. Zhang, X.-H. Shi et al., “Predicting drug-target interaction networks based on functional groups and biological features,” PLoS ONE, vol. 5, no. 3, Article ID e9603, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Chen, Z.-S. He, T. Huang, and Y.-D. Cai, “Using compound similarity and functional domain composition for prediction of drug-target interaction networks,” Medicinal Chemistry, vol. 6, no. 6, pp. 388–395, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Chen and W.-M. Zeng, “A two-step similarity-based method for prediction of drugs target group,” Protein and Peptide Letters, vol. 20, pp. 364–370, 2013.
  9. M. Campillos, M. Kuhn, A.-C. Gavin, L. J. Jensen, and P. Bork, “Drug target identification using side-effect similarity,” Science, vol. 321, no. 5886, pp. 263–266, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Ogata, S. Goto, K. Sato, W. Fujibuchi, H. Bono, and M. Kanehisa, “KEGG: kyoto encyclopedia of genes and genomes,” Nucleic Acids Research, vol. 27, no. 1, pp. 29–34, 1999. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Kuhn, C. von Mering, M. Campillos, L. J. Jensen, and P. Bork, “STITCH: interaction networks of chemicals and proteins,” Nucleic Acids Research, vol. 36, no. 1, pp. D684–D688, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Degtyarenko, P. De matos, M. Ennis et al., “ChEBI: a database and ontology for chemical entities of biological interest,” Nucleic Acids Research, vol. 36, no. 1, pp. D344–D350, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Chen, W.-M. Zeng, Y.-D. Cai, K.-Y. Feng, and K.-C. Chou, “Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities,” PLoS ONE, vol. 7, no. 4, Article ID e35254, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. L.-L. Hu, C. Chen, T. Huang, Y.-D. Cai, and K.-C. Chou, “Predicting biological functions of compounds based on chemical-chemical interactions,” PLoS ONE, vol. 6, no. 12, Article ID e29491, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Chen, T. Huang, J. Zhang, et al., “Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions,” BioMed Research International, vol. 2013, Article ID 485034, 8 pages, 2013. View at Publisher · View at Google Scholar
  16. L. Chen, X. Shi, X. Kong, Z. Zeng, and Y.-D. Cai, “Identifying protein complexes using hybrid properties,” Journal of Proteome Research, vol. 8, no. 11, pp. 5212–5218, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at Scopus
  18. K.-C. Chou and Y.-D. Cai, “Prediction of protein subcellular locations by GO-FunD-PseAA predictor,” Biochemical and Biophysical Research Communications, vol. 320, no. 4, pp. 1236–1239, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. C. E. Jones, U. Baumann, and A. L. Brown, “Automated methods of predicting the function of biological sequences using GO and BLAST,” BMC Bioinformatics, vol. 6, article 272, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. M. A. Mahdavi and Y.-H. Lin, “False positive reduction in protein-protein interaction predictions using gene ontology annotations,” BMC Bioinformatics, vol. 8, article 262, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Carroll and V. Pavlovic, “Protein classification using probabilistic chain graphs and the Gene Ontology structure,” Bioinformatics, vol. 22, no. 15, pp. 1871–1878, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Smith, W. Ceusters, B. Klagges et al., “Relations in biomedical ontologies,” Genome biology, vol. 6, no. 5, article R46, 2005. View at Scopus
  23. L. Hu, T. Huang, X. Shi, W.-C. Lu, Y.-D. Cai, and K.-C. Chou, “Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties,” PLoS ONE, vol. 6, no. 1, Article ID e14556, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. R. Sharan, I. Ulitsky, and R. Shamir, “Network-based prediction of protein function,” Molecular systems biology, vol. 3, p. 88, 2007. View at Scopus
  25. K.-L. Ng, J.-S. Ciou, and C.-H. Huang, “Prediction of protein functions based on function-function correlation relations,” Computers in Biology and Medicine, vol. 40, no. 3, pp. 300–305, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. X. Shao, Y. Tian, L. Wu, Y. Wang, L. Jing, and N. Deng, “Predicting DNA- and RNA-binding proteins from sequences with kernel methods,” Journal of Theoretical Biology, vol. 258, no. 2, pp. 289–293, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. D. N. Georgiou, T. E. Karakasidis, J. J. Nieto, and A. Torres, “Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition,” Journal of Theoretical Biology, vol. 257, no. 1, pp. 17–26, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. X. Xiao, J. Min, and P. Wang, “Predicting ion channel-drug interactions based on sequence-derived features and functional groups,” Journal of Bionanoscience, vol. 7, pp. 49–54, 2013.
  29. R. G. Ramani and S. G. Jacob, “Prediction of P53 mutants (multiple sites) transcriptional activity based on structural (2D&3D) properties,” PLoS ONE, vol. 8, Article ID e55401, 2013.
  30. G. S. Han, V. Anh, A. P. Krishnajith, and Y.-C. Tian, “An ensemble method for predicting subnuclear localizations from primary protein structures,” PLoS ONE, vol. 8, Article ID e57225, 2013.
  31. Y. Matsuta, M. Ito, and Y. Tohsato, “ECOH: an enzyme commission number predictor using mutual information and a support vector machine,” Bioinformatics, vol. 29, pp. 365–372, 2013.
  32. Z. Qiu, C. Qin, M. Jiu, and X. Wang, “A simple iterative method to optimize protein ligand-binding residue prediction,” Journal of Theoretical Biology, vol. 317, pp. 219–223, 2012.
  33. Y.-N. Zhang, D.-J. Yu, S.-S. Li, et al., “Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features,” BMC Bioinformatics, vol. 13, article 118, 2012.
  34. W. Chen and H. Lin, “Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine,” Computers in Biology and Medicine, vol. 42, no. 4, pp. 504–507, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. L. Chen, W. Zeng -M, Y. Cai -D, and T. Huang, “Prediction of metabolic pathway using graph property, chemical functional group and chemical structural set,” Current Bioinformatics, vol. 8, pp. 200–207, 2013.
  36. T. Huang, L. Chen, Y.-D. Cai, and K.-C. Chou, “Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property,” PLoS ONE, vol. 6, no. 9, Article ID e25297, 2011. View at Publisher · View at Google Scholar · View at Scopus