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Research Letters in Signal Processing
Volume 2008, Article ID 365152, 5 pages
http://dx.doi.org/10.1155/2008/365152
Research Letter

Is Subcellular Localization Informative for Modeling Protein-Protein Interaction Signal?

1Division of Biometrics, The Cancer Institute of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08901, USA
2Department of Biostatistics, School of Public Health, University of Medicine and Dentistry of New Jersey, 683 Hoes Lane West, Piscataway, NJ 08854, USA
3Department of Epidemiology and Public Health, Yale University School of Medicine, 60 College Street, New Haven, CT 06520, USA
4Department of Statistics, West Virginia University, P.O. Box 6330, Morgantown, WV 26506, USA
5Department of Electronic and Computer Engineering, The Hong Kong University of Sciences and Technology, Clear Water Bay, Kowloon, Hong Kong, China

Received 21 August 2007; Accepted 2 January 2008

Academic Editor: Jar-Ferr Kevin Yang

Copyright © 2008 Junfeng Liu 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. H. W. Mewes, D. Frishman, C. Gruber et al., “MIPS: a database for genomes and protein sequences,” Nucleic Acids Research, vol. 28, no. 1, pp. 37–40, 2000. View at Publisher · View at Google Scholar
  2. N. Lin and H. Zhao, “Are scale-free networks robust to measurement errors?” BMC Bioinformatics, vol. 6, no. 1, p. 119, 2005. View at Publisher · View at Google Scholar
  3. W.-K Huh, J. V. Falvo, L. C. Gerke et al., “Global analysis of protein localization in budding yeast,” Nature, vol. 425, pp. 686–691, 2003. View at Publisher · View at Google Scholar
  4. P. McCullagh and J. A. Nelder, Generalized Linear Models, Chapman & Hall, London, UK, 2nd edition, 1989.
  5. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar
  6. Y. Liu, N. Liu, and H. Zhao, “Inferring protein-protein interactions through high-throughput interaction data from diverse organisms,” Bioinformatics, vol. 21, no. 15, pp. 3279–3285, 2005. View at Publisher · View at Google Scholar
  7. Z. Lu, D. Szafron, R. Greiner et al., “Predicting subcellular localization of proteins using machine-learned classifiers,” Bioinformatics, vol. 20, no. 4, pp. 547–556, 2004. View at Publisher · View at Google Scholar
  8. D. Szafron, P. Lu, R. Greiner et al., “Proteome analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations,” Nucleic Acids Research, vol. 32, Web Server issue, pp. W365–W371, 2004. View at Publisher · View at Google Scholar
  9. A. Höglund, P. Dönnes, T. Blum, H.-W. Adolph, and O. Kohlbacher, “MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition,” Bioinformatics, vol. 22, no. 10, pp. 1158–1165, 2006. View at Publisher · View at Google Scholar
  10. P. Horton, K.-J Park, T. Obayashi, and K. Nakai, “Protein subcellular localization prediction with WoLF PSORT,” in Proceedings of the 4th Asia-Pacific Bioinformatics Conference (APBC '06), pp. 39–48, Taipei, Taiwan, February 2006. View at Publisher · View at Google Scholar
  11. C. Guda, “pTARGET: a web server for predicting protein subcellular localization,” Nucleic Acids Research, vol. 34, Web Server issue, pp. W210–W213, 2006. View at Publisher · View at Google Scholar
  12. C.-S. Yu, Y.-C. Chen, C.-H. Lu, and J.-K. Hwang, “Prediction of protein subcellular localization,” Proteins, vol. 64, no. 3, pp. 643–651, 2006. View at Publisher · View at Google Scholar
  13. T. Zhang, Y. Ding, and S. Shao, “Protein subcellular location prediction based on pseudo amino acid composition and immune genetic algorithm,” in Proceedings of the International Conference on Intelligent Computing (ICIC '06), vol. 4115, part 3, pp. 534–542, Kunming, China, August 2006. View at Publisher · View at Google Scholar
  14. T. Zhang, Y. Ding, and K.-C. Chou, “Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence,” Computational Biology and Chemistry, vol. 30, no. 5, pp. 367–371, 2006. View at Publisher · View at Google Scholar