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Part of the book series: Use R! ((USE R))

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Abstract

The development of new and innovative treatments for unmet medical (Barlow et al. 1972) needs is the major challenge in biomedical research. Unfortunately, for the past decade, there has been a steady decline in the number of new therapies reaching the market, despite of the increased investments in pharmaceutical R&D (FDA 2004). One of the most critical steps in a drug discovery program is target identification and validation (Sams-Dodd 2005).

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References

  • Amaratunga & Cabrera, J. (2003). Exploration and Analysis of DNA Microarray and Protein Array Data, New York: John Wiley

    Google Scholar 

  • Barlow, R.E., Bartholomew, D.J., Bremner, M.J., & Brunk, H.D. (1972). Statistical inference under order restriction. New York: Wiley.

    Google Scholar 

  • Butcher, S. (2003). Target discovery and validation in the post-genomic era. Neurochemical Research, 28, 367–377.

    Google Scholar 

  • Chuang-Stein, C., & Agresti, A. (1997). Tutorial in biostatistics: A review of tests for detecting a monotone dose-response relationship with ordinal response data. Statistics in Medicine, 16, 2599–2618.

    Google Scholar 

  • Cooke, R.M. (Ed.). (2009). Uncertainty modeling in dose-response. New York: Wiley.

    Google Scholar 

  • FDA, U. (2004). Innovation or stagnation: Challenge and opportunity on the critical path to new medical products. Silver Spring: US Food and Drug Administation. New York: Wiley

    Google Scholar 

  • Goehlmann, H., & Talloen, W. (2009). Gene expression studies using Affymetrix microarrays. Boca Raton: Chapman & Hall/CRC. New York: Wiley

    Google Scholar 

  • Jacqmin, P., Snoeck, E., van Schaick, E., Gieschke, R., Pillai, P., Steimer, J.-L., et al. (2007). Modelling response time profiles in the absence of drug concentrations: Definition and performance evaluation of the KPD model. Journal of Pharmacokinetics and Pharmacodynamics, 34, 57–85.

    Google Scholar 

  • Jacobs, T., Straetmans, R., Molenberghs, G., Bouwknecht, A., & Bijnens, L. (2010). Latent pharmacokinetic time profile to model dose-response survival data. Journal of Biopharmaceutical Statistics, 20(4), 759–767.

    Google Scholar 

  • Lengauer, T. (2001). Computational biology at the beginning of the post-genomic era. In Informatics—10 years back. 10 years ahead (Vol. 355). Heidelberg: Springer. New York: Wiley.

    Google Scholar 

  • Louis, T.A. (2009). Math/Stat perspective in Chapter 2: agreement and disagreement, in Cooke, R.M. (Ed.). (2009). Uncertainty modeling in dose-response. New York: Wiley.

    Google Scholar 

  • Marton, M., DeRisi, J., Bennett, H., Iyer, V., Meyer, M. Roberts, C., Stoughton, R., Burchard, J., Slade, D., & Dai, H. (1998). Drug target validation and idenfication of secondary drug target effects using DNA microarrays. Nature Medicine, 4, 1293–1301.

    Google Scholar 

  • Ruberg, S.J. (1995a). Dose response studies. I. Some design considerations. Journal of Biopharmaceutical Statistics, 5(1), 1–14.

    Google Scholar 

  • Ruberg, S. J. (1995b) Dose response studies. II. Analysis and interpretation. Journal of Biopharmaceutical Statistics, 5(1), 15–42.

    Google Scholar 

  • Sams-Dodd, F. (2005). Target-based drug discovery: Is something wrong? Drug Discovery Today, 10, 139–147.

    Google Scholar 

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Correspondence to Dan Lin .

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© 2012 Springer-Verlag Berlin Heidelberg

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Lin, D., Talloen, W., Bijnens, L., Göhlmann, H.W.H., Amaratunga, D., Straetemans, R. (2012). Introduction. In: Lin, D., Shkedy, Z., Yekutieli, D., Amaratunga, D., Bijnens, L. (eds) Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. Use R!. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24007-2_1

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