Building Process Understanding for Vaccine Manufacturing Using Data Mining

REFERENCES

• Almond , J. W. ( 2007 ). Vaccine renaissance . Nature Reviews Microbiology , 5 : 478 – 481 .
   PubMed Web of Science ®Google Scholar
• Biological Products: General. Code of Federal Regulations Title 18, pt. 600.3, 2009 .
   Google Scholar
• Bisgaard , S. , Kulahci , M. ( 2005 ). Quality Quandaries: The effect of autocorrelation on statistical process control procedures . Quality Engineering , 17 : 481 – 489 .
   Google Scholar
• Breiman , L. ( 2001 ). Random forests . Machine Learning , 45 ( 1 ): 5 – 32 .
   Web of Science ®Google Scholar
• Breiman , L. , Friedman , J. H. , Olshen , R. A. , Stone , C. J. ( 1984 ). Classification and Regression Trees . Belmont , CA : Wadsworth .
   Google Scholar
• De Mast , J. , Trip , A. ( 2007 ). Exploratory data analysis in quality-improvement projects . Journal of Quality Technology , 39 ( 4 ): 301 – 311 .
   Web of Science ®Google Scholar
• Efron , B. , Tibshirani , R. ( 1993 ). An Introduction to the Bootstrap . New York : Chapman & Hall/CRC .
   Google Scholar
• Garcia-Munoz , S. , Kourti , T. , MacGregor , J. F. , Mateos , A. G. , Murphy , G. ( 2003 ). Troubleshooting of an industrial batch process using multivariate methods . Industrial & Engineering Chemistry Research , 42 : 3592 – 3601 .
   Web of Science ®Google Scholar
• Geladi , P. , Kowalski , B. R. ( 1986 ). Partial least squares regression: A tutorial . Analytica Chimica Acta , 185 ( 1 ): 1 – 17 .
   Google Scholar
• Höskuldsson , A. ( 1996 ). Prediction Methods in Science and Technology . Copenhagen : Thor Publishing .
   Google Scholar
• Hothorn , T. , Hornik , K. , Zeileis , A. ( 2006 ). Unbiased recursive partitioning: A conditional inference framework . Journal of Computational and Graphical Statistics , 15 ( 3 ): 651 – 674 .
   Web of Science ®Google Scholar
• Kuncheva , L. I. ( 2004 ). Combining Pattern Classifiers . Hoboken , NJ : Wiley .
   Google Scholar
• Liaw , A. , Wiener , M. ( 2002 ). Classification and regression by random Forest . R News , 2/3 : 8 – 22 .
   Google Scholar
• Lorber , A. , Wangen , L. E. , Kowalski , B. R. ( 1987 ). A theoretical foundation for the PLS algorithm . Journal of Chemometrics , 1 : 19 – 31 .
   Google Scholar
• Martens , H. , Karstang , T. , Naes , T. ( 1987 ). Improved selectivity in spectroscopy by multivariate calibration . Journal of Chemometrics , 1 ( 4 ): 201 – 219 .
   Google Scholar
• Potter , C. ( 2009 ). Product quality lifecycle implementation (PQLI) innovations application of science- and risk-based approaches (ICH Q8, Q9, and Q10) to existing products . Journal of Pharmaceutical Innovation , 4 : 4 – 25 .
   Web of Science ®Google Scholar
• R Development Core Team . ( 2008 ). R: A Language and Environment for Statistical Computing Vienna , Austria : R Foundation for Statistical Computing .
   Google Scholar
• Wold , S. , Albano , C. , Dunn , W. J. , III , Esbensen , K. , Hellberg , S. , Johansson , E. , Lindberg , W. , Sjoestroem , M. ( 1984 ). Modeling data tables by principal components and PLS: Class patterns and quantitative predictive relations . Analusis , 12 ( 10 ): 477 – 485 .
   Google Scholar