Abstract
Drug efficacy is multifactorial and with multiple variables regression modeling rapidly looses power and it is invalid if the correlations between the variables is strong. Hierarchical cluster analysis can handle hundreds of variables, and is unaffected by strong correlations.
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References
Solyanik GI (2010) Multifactorial nature of tumor drug resistance. Exp Oncol 32:181–185
Tsao DA, Chang HJ, Hsiung SK, Huang SE, Chang MS, Chiu HH, Chen YF, Cheng TL, Shiu-Ru L (2010) Gene expression profiles for predicting the efficacy of the anticancer drug 5-fluorouracil in breast cancer. DNA Cell Biol 29:285–293
Latan MS, Laddha NC, Latani J, Imran MJ, Begum R, Misra A (2012) Suppression of cytokine gene expression and improved therapeutic efficacy of microemulsion -based tacrolimus cream for atopic dermatitis. Drug Deliv Transl Res 2:129–141
National Institutes of Health, 9000 Rockville Pike, Bethesda, Maryland. Enhancing HIV Âvaccine efficacy in high-risk drug users. Release data Jan 6 2003, RFA Number DA-03-002
Alsford S, Eckert S, Baker N, Glover L, Sanchez-Flores A, Leung KF, Turner DJ, Field MC, Berriman M, Horn D (2012) High throughput decoding of antitrypanosomal drug efficacy and resistance. Nature. doi:10.1038/nature10771
Sibson R (1973) An optimally efficient algorithm for a single-link cluster method. Comput J 16:30–34
Defays D (1977) An efficient algorithm for a complete link cluster method. Comput J 20:364–366
Anonymous (2012) Cluster analysis. http://en.wikipedia.org/Clustert_analysis. Accessed 12 July 2012
Anonymous (2012) Sequence clustering. http://en.wikipedia.org/wiki/Sequence_analysis. Accessed 12 July 2012
Kim HK, Choi IJ, Kim HS, Oshima A, Michalowski A, Green JE (2011) A gene expression signature of acquired chemoresistance to cisplatinum and fluorouracil combination chemotherapy in gastric cancer patients. PLoS One 18:e16694
Yeh ST (2012) Clinical adverse event data analysis and visualization. Smith Kline Datafile, Accessed 10 July 2012
Bychowiec B, Piskorski J, Stanislawska K, Dziarmaga M, Mineczykowski A, Wykretowicz A, Wysocki H (2010) An exploratory clustering study of rare adverse events in drug deluting stent patients. Comput Methods Sci Technol 16:5–11
Xu D, Redamn-Furey N (2007) Statistical cluster analysis of pharmaceutical solvents. Int J Pharm 339:175–188
Hawwa A, Millership JS, Collier PS, McCarthy A, Dempsey S, Cairns C, McElnay JC (2009) Development of objective methodology to measure medication adherence to oral thiopurines in paediatric patients with acute lymphoblastic leucemia. Eur J Clin Pharm 65:1105–1112
SPSS statistical software (2012) www.spss.com. Accessed 12 July 2012
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Cleophas, T.J., Zwinderman, A.H. (2013). Hierarchical Cluster Analysis for Unsupervised Data. In: Machine Learning in Medicine. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5824-7_15
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DOI: https://doi.org/10.1007/978-94-007-5824-7_15
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