Chémobiologie
Free Access
Issue
Med Sci (Paris)
Volume 31, Number 2, Février 2015
Chémobiologie
Page(s) 187 - 196
Section M/S Revues
DOI https://doi.org/10.1051/medsci/20153102016
Published online 04 March 2015
  1. Carragher NO, Brunton VG, Frame MC. Combining imaging and pathway profiling: an alternative approach to cancer drug discovery. Drug Discov Today 2012 ; 17 : 203–214. [CrossRef] [PubMed] [Google Scholar]
  2. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004 ; 3 : 711–715. [CrossRef] [PubMed] [Google Scholar]
  3. Prudent R, Soleilhac E, Barette C, et al. Les criblages phénotypiques ou comment faire d’une pierre deux coups : découvrir la cible et la molécule pharmacologique capable de la réguler. Med Sci (Paris) 2013 ; 29 : 897–905. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  4. Perrimon N, Friedman A, Mathey-Prevot B, Eggert US. Drug-target identification in Drosophila cells: combining high-throughout RNAi and small-molecule screens. Drug Discov Today 2007 ; 12 : 28–33. [CrossRef] [PubMed] [Google Scholar]
  5. Perrimon N, Mathey-Prevot B. Applications of high-throughput RNA interference screens to problems in cell and developmental biology. Genetics 2007 ; 175 : 7–16. [CrossRef] [PubMed] [Google Scholar]
  6. Rines DR, Tu B, Miraglia L, et al. High-content screening of functional genomic libraries. Methods Enzymol 2006 ; 414 : 530–565. [CrossRef] [PubMed] [Google Scholar]
  7. Zock JM. Applications of high content screening in life science research. Comb Chem High Throughput Screen 2009 ; 12 : 870–876. [CrossRef] [PubMed] [Google Scholar]
  8. Mouchet EH, Simpson PB. High-content assays in oncology drug discovery: opportunities and challenges. IDrugs 2008 ; 11 : 422–427. [PubMed] [Google Scholar]
  9. Berns K, Hijmans EM, Mullenders J, et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 2004 ; 428 : 431–437. [CrossRef] [PubMed] [Google Scholar]
  10. Ganesan AK, Ho H, Bodemann B, et al. Genome-wide siRNA-based functional genomics of pigmentation identifies novel genes and pathways that impact melanogenesis in human cells. PLoS Genet 2008 ; 4 : e1000298. [CrossRef] [PubMed] [Google Scholar]
  11. Eggert US, Mitchison TJ. Small molecule screening by imaging. Curr Opin Chem Biol 2006 ; 10 : 232–237. [CrossRef] [PubMed] [Google Scholar]
  12. Korn K, Krausz E. Cell-based high-content screening of small-molecule libraries. Curr Opin Chem Biol 2007 ; 11 : 503–510. [CrossRef] [PubMed] [Google Scholar]
  13. Perlman ZE, Slack MD, Feng Y, et al. Multidimensional drug profiling by automated microscopy. Science 2004 ; 306 : 1194–1198. [CrossRef] [PubMed] [Google Scholar]
  14. Giuliano KA. Optimizing the integration of immunoreagents and fluorescent probes for multiplexed high content screening assays. Methods Mol Biol 2007 ; 356 : 189–193. [PubMed] [Google Scholar]
  15. Young DW, Bender A, Hoyt J, et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat Chem Biol 2008 ; 4 : 59–68. [CrossRef] [PubMed] [Google Scholar]
  16. Maréchal E, Roy S, Lafanechère L. Chemogénomique : des petites molécules pour explorer le vivant. Une introduction à l’usage des biologistes, chimistes et informaticiens. Paris : EDP Sciences, 2007 : 258 p. [Google Scholar]
  17. Walter T, Held M, Neumann B, et al. Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging. J Struct Biol 2010 ; 170 : 1–9. [CrossRef] [PubMed] [Google Scholar]
  18. Fenistein D, Lenseigne B, Christophe T, et al. A fast, fully automated cell segmentation algorithm for high-throughput and high-content screening. Cytometry A 2008 ; 73 : 958–964. [CrossRef] [PubMed] [Google Scholar]
  19. Shariff A, Kangas J, Coelho LP, et al. Automated image analysis for high-content screening and analysis. J Biomol Screen 2010 ; 15 : 726–734. [CrossRef] [PubMed] [Google Scholar]
  20. Horvath P, Wild T, Kutay U, Csucs G. Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results. J Biomol Screen 2011 ; 16 : 1059–1067. [CrossRef] [PubMed] [Google Scholar]
  21. Carpenter AE, Jones TR, Lamprecht MR, et al. Cell Profiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 2006 ; 7 : R100. [CrossRef] [PubMed] [Google Scholar]
  22. Walter T, Shattuck DW, Baldock R, et al. Visualization of image data from cells to organisms. Nat Methods 2010 ; 7 : S26–S41. [CrossRef] [PubMed] [Google Scholar]
  23. Soleilhac E, Nadon R, Lafanechere L. High-content screening for the discovery of pharmacological compounds: advantages, challenges and potential benefits of recent technological developments. Expert Opin Drug Discov 2010 ; 5 : 135–144. [CrossRef] [PubMed] [Google Scholar]
  24. Loo LH, Lin HJ, Steininger RJ 3rd, et al. An approach for extensibly profiling the molecular states of cellular subpopulations. Nat Methods 2009 ; 6 : 759–765. [CrossRef] [PubMed] [Google Scholar]
  25. Loo LH, Wu LF, Altschuler SJ. Image-based multivariate profiling of drug responses from single cells. Nat Methods 2007 ; 4 : 445–453. [PubMed] [Google Scholar]
  26. Neumann B, Held M, Liebel U, et al. High-throughput RNAi screening by time-lapse imaging of live human cells. Nat Methods 2006 ; 3 : 385–390. [CrossRef] [PubMed] [Google Scholar]
  27. Hamilton NA, Pantelic RS, Hanson K, Teasdale RD. Fast automated cell phenotype image classification. BMC Bioinformatics 2007 ; 8 : 110. [CrossRef] [PubMed] [Google Scholar]
  28. Jones TR, Carpenter AE, Lamprecht MR, et al. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci USA 2009 ; 106 : 1826–1831. [CrossRef] [Google Scholar]
  29. Smith K, Horvath P. Active learning strategies for phenotypic profiling of high-content Screens. J Biomol Screen 2014 ; 19 : 685–695. [CrossRef] [PubMed] [Google Scholar]
  30. Brideau C, Gunter B, Pikounis B, Liaw A. Improved statistical methods for hit selection in high-throughput screening. J Biomol Screen 2003 ; 8 : 634–647. [CrossRef] [PubMed] [Google Scholar]
  31. Kevorkov D, Makarenkov V. Statistical analysis of systematic errors in high-throughput screening. J Biomol Screen 2005 ; 10 : 557–567. [CrossRef] [PubMed] [Google Scholar]
  32. Malo N, Hanley JA, Cerquozzi S, et al. Statistical practice in high-throughput screening data analysis. Nat Biotechnol 2006 ; 24 : 167–175. [CrossRef] [PubMed] [Google Scholar]
  33. Gagarin A, Makarenkov V, Zentilli P. Using clustering techniques to improve hit selection in high-throughput screening. J Biomol Screen 2006 ; 11 : 903–914. [CrossRef] [PubMed] [Google Scholar]
  34. Makarenkov V, Zentilli P, Kevorkov D, et al. An efficient method for the detection and elimination of systematic error in high-throughput screening. Bioinformatics 2007 ; 23 : 1648–1657. [CrossRef] [PubMed] [Google Scholar]
  35. Kozak K, Agrawal A, Machuy N, Csucs G. Data mining techniques in high content screening: A survey. J Comput Sci Syst Biol 2009 ; 2 : 219–239. [CrossRef] [Google Scholar]
  36. Carey KL, Westwood NJ, Mitchison TJ, Ward GE. A small-molecule approach to studying invasive mechanisms of Toxoplasma gondii. Proc Natl Acad Sci USA 2004 ; 101 : 7433–7438. [CrossRef] [Google Scholar]
  37. Peterson RT, Fishman MC. Discovery and use of small molecules for probing biological processes in zebrafish. Methods Cell Biol 2004 ; 76 : 569–591. [CrossRef] [PubMed] [Google Scholar]
  38. Simpson KJ, Davis GM, Boag PR. Comparative high-throughput RNAi screening methodologies in C. elegans and mammalian cells. New Biotechnol 2012 ; 29 : 459–470. [CrossRef] [Google Scholar]
  39. Wählby C, Kamentsky L, Liu ZH, et al. An image analysis toolbox for high-throughput C. elegans assays. Nat Methods 2012 ; 9 : 666–670. [CrossRef] [PubMed] [Google Scholar]
  40. LaBarbera DV, Reid BG, Yoo BH. The multicellular tumor spheroid model for high-throughput cancer drug discovery. Expert Opin Drug Discov 2012 ; 7 : 819–830. [CrossRef] [PubMed] [Google Scholar]
  41. Li Q, Chen C, Kapadia A, et al. 3D models of epithelial-mesenchymal transition in breast cancer metastasis: high-throughput screening assay development, validation, and pilot screen. J Biomol Screen 2011 ; 16 : 141–154. [CrossRef] [PubMed] [Google Scholar]
  42. Breslin S, O’Driscoll L. Three-dimensional cell culture: the missing link in drug discovery. Drug Discov Today 2013 ; 18 : 240–249. [Google Scholar]
  43. Rimann M, Graf-Hausner U. Synthetic 3D multicellular systems for drug development. Curr Opin Biotechnol 2012 ; 23 : 803–809. [CrossRef] [PubMed] [Google Scholar]
  44. Wang T, Wei JJ, Sabatini DM, Lander ES. Genetic screens in human cells using the CRISPR-Cas9 system. Science 2014 ; 343 : 80–84. [CrossRef] [PubMed] [Google Scholar]
  45. Shalem O, Sanjana NE, Hartenian E, et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 2014 ; 343 : 84–87. [CrossRef] [PubMed] [Google Scholar]
  46. De Souza A, Bittker JA, Lahr DL, et al. An overview of the challenges in designing, Integrating, and delivering BARD: a public chemical-biology resource and query portal for multiple organizations, locations, and disciplines. J Biomol Screen 2014 ; 19 : 614–627. [CrossRef] [PubMed] [Google Scholar]
  47. Dupret B, Angrand PO. L’ingénierie des génomes par les TALEN. Med Sci (Paris) 2014 ; 30 : 186–193. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  48. Gilgenkrantz H.. La révolution des CRISPR est en marche. Med Sci (Paris) 2014 ; 30 : 1066–1069. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  49. Asensio C.. Application de la méthode Cas9/SRISPR à l’étude de la fonction synaptique. Med Sci (Paris) 2015 ; 31 : 137–138. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.