About this Journal Submit a Manuscript Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 156932, 11 pages
http://dx.doi.org/10.1155/2013/156932
Methodology Report

1Click1View: Interactive Visualization Methodology for RNAi Cell-Based Microscopic Screening

1Nauru, LTD., Breslau, Poland
2Technical University of Dresden, 01307 Dresden, Germany

Received 24 September 2012; Accepted 31 October 2012

Academic Editor: Mouldy Sioud

Copyright © 2013 Lukasz Zwolinski 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. A. Dove, “High-throughput screening goes to school,” Nature Methods, vol. 4, no. 6, pp. 523–532, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. G. J. Hannon, “RNA interference,” Nature, vol. 418, no. 6894, pp. 244–251, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Shneiderman, “The eyes have it: a task by data type taxonomy for information visualizations,” in Proceedings of the IEEE Symposium on Visual Languages (VL '96), pp. 336–343, IEEE Computer Society, September 1996. View at Scopus
  4. J. Khan, J. S. Wei, M. Ringnér et al., “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nature Medicine, vol. 7, no. 6, pp. 673–679, 2001. View at Publisher · View at Google Scholar · View at Scopus
  5. G. Leban, B. Zupan, G. Vidmar, and I. Bratko, “VizRank: data visualization guided by machine learning,” Data Mining and Knowledge Discovery, vol. 13, no. 2, pp. 119–136, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. J. F. McCarthy, K. A. Marx, P. E. Hoffman et al., “Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management,” Annals of the New York Academy of Sciences, vol. 1020, pp. 239–262, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. KNIME, University at Konstanz, http://knime.org/.
  8. P. A. Johnston and P. A. Johnston, “Cellular platforms for HTS: three case studies,” Drug Discovery Today, vol. 7, no. 6, pp. 353–363, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. K. A. Giuliano, J. R. Haskins, and D. L. Taylor, “Advances in high content screening for drug discovery,” Assay and Drug Development Technologies, vol. 1, no. 4, pp. 565–577, 2003. View at Scopus
  10. D. L. Taylor, E. S. Woo, and K. A. Giuliano, “Real-time molecular and cellular analysis: the new frontier of drug discovery,” Current Opinion in Biotechnology, vol. 12, no. 1, pp. 75–81, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. A. J. Monk, “Faster, surer prediction,” The Biochemist, vol. 10, pp. 25–28, 2005.
  12. C. Brideau, B. Gunter, B. Pikounis, and A. Liaw, “Improved statistical methods for hit selection in high-throughput screening,” Journal of Biomolecular Screening, vol. 8, no. 6, pp. 634–647, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Heyse, “Comprehensive analysis of high-throughput screening data,” in Biomedical Nanotechnology Architectures and Applications, Proceedings of SPIE, pp. 535–547, January 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. J. H. Zhang, T. D. Y. Chung, and K. R. Oldenburg, “A simple statistical parameter for use in evaluation and validation of high throughput screening assays,” Journal of Biomolecular Screening, vol. 4, no. 2, pp. 67–73, 1999. View at Publisher · View at Google Scholar · View at Scopus
  15. J. H. Zhang, T. D. Y. Chung, and K. R. Oldenburg, “Confirmation of primary active substances from high throughput screening of chemical and biological populations: a statistical approach and practical considerations,” Journal of Combinatorial Chemistry, vol. 2, no. 3, pp. 258–265, 2000. View at Scopus
  16. B. Cox, J. C. Denyer, A. Binnie et al., “Application of high-throughput screening techniques to drug discovery,” Progress in Medicinal Chemistry, vol. 37, pp. 83–133, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Lutz and T. Kenakin, Quantitative Molecular Pharmacology and Informatics in Drug Discovery, John Wiley & Sons, New York, NY, USA, 2000.
  18. Advanced Cell Classifier, http://acc.ethz.ch/.
  19. C. Lin, W. Mak, P. Hong, K. Sepp, and N. Perrimon, “Intelligent interfaces for mining large-scale RNAi-HCS image databases,” in Proceedings of the IEEE International Symposium on Bioinformatics and Bioengineering, 2007.
  20. A. Smellie, C. J. Wilson, and S. C. Ng, “Visualization and interpretation of high content screening data,” Journal of Chemical Information and Modeling, vol. 46, no. 1, pp. 201–207, 2006.
  21. H. Kang and B. Shneiderman, “Visualization methods for personal photo collections: browsing and searching in the PhotoFinder,” in Proceedings of the IEEE International Conference on Multimedia and Expo, New York, NY, USA, 2000.
  22. B. Moghaddam, Q. Tian, N. Lesh, C. Shen, and T. S. Huang, “Visualization and user-modeling for browsing personal photo libraries,” International Journal of Computer Vision, vol. 56, no. 1-2, pp. 109–130, 2004.
  23. I. G. Goldberg, C. Allan, J.-M. Burel, et al., “The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging,” Genome Biology, vol. 6, no. 5, article R47, 2005.
  24. T. Wild, P. Horvath, E. Wyler, et al., “A protein inventory of human ribosome biogenesis reveals an essential function of Exportin 5 in 60S subunit export,” PLOS Biology, vol. 8, no. 10, Article ID e1000522.