Abstract
This paper presents a modification to the temporal disease clusters methods in order to apply them to other sciences. Particular specifications are shown in Scan and temporal Grimson techniques. The original data are transformed in a binary sequence: the value “one” represents the interest category and value “zero” corresponds to all other cases. Computational transformations of the algorithms are implemented
using Mathematica Package. Besides some interesting bioinformatics applications are presented.
References
Alakurtti, K.; Weber, E.; Rinne, R.; Theil, G.; Lindhout, D.; Salmikangas, P.; Saukko, P.; Lahtinen, U. (2005) “Loss of lysosomal association of cystatin B proteins representing progressive myoclonus epilepsy, EPM1, mutations”, Hum Genet 13: 208–215.
Altschul, S.F. (1996) “Local alignment statistics”, Meth. Enzymol 274: 460–480.
Altschul, S. F.; Madden, T. L.; Schaffer, A. A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, L. J. (1997) “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs”, Nucl. Acids Res. 25: 3389–3402.
Bailey, N. T. J. (1975). The mathematical theory of infectious diseases and its applications. Charles Griffin & Company LTD, Second Edition.
Casas, G; Grau, R. (2001) “Validación de dos métodos de detección de conglomerados temporales usando un modelo de epidemia simple”, Investigación Ooperacional 6(2): 175–187.
Casas, G. (2003) Técnicas de Detección de Conglomerados Incluyendo Factores Adicionales. Tesis de Doctorado, Universidad Central de Las Villas, Cuba.
Casas, G.; Grau, R.; Cardoso, G. (2004) “Introducción de factores de riesgo en los métodos de Knox y Grimson para el estudio de conglomerados espaciotemporales”, Revista de Matemática: Teoría y Aplicaciones 11(1): 69–80.
Gentle, J.E.; Hardle, W.; Mori, Y. (2004) Handbook of Computacional Statistics. Springer, Heidelberg.
Grimson, R. (1993) “Disease clusters, exact distributions of maxima and p-values”, Statistics in Medicine 12: 1773–1794.
Grimson, R.; Rose, R. (1991) “A versatile test for clustering and a proximity analysis of neurons”, Meth. Inform. in Med. 30: 299–303.
Grimson, R. (1994) “Disease cluster test based on the maximum occupancy frequency”, Proceedings of the section on Epidemiology, American Statistical Association: 64–69.
Jacquez, G.; Waller, L. (1964) “Disease cluster statistics for imprecise space-time locations”, Statistics in Medicine 15: 873–85.
Jacquez, G. (1996) “The analysis of disease clusters, part I: state of the art” , Infection Control and Hospital Epidemiology 17(5): 319–327.
Jacquez, G. (1996) “The analysis of disease clusters, part II: introduction to techniques”, Infection Control and Hospital Epidemiology 17(6): 385–397.
Jain, A.K.; Murty, M.N.; Flynn, P.J. (1999) “Data clustering: a review”, ACM Computing Surveys 31(3): 264–323.
Knox, E. (1964) “The detection of spece-time interactions” , Appl. Statist. 13: 25–29.
Langrand, C. (2005) “Scan Statistics: definición y ejemplos”, Seminario ANY 2005. Universidad Politécnica de Cataluya. España.
Nagarwilla, N. (1996) “A Scan statistic with a variable window”, Stat. in Med. 15: 845–850.
Nauss, J. I. (1982) “Approximations for distributions of Scan statistics”, Journal of the American Statistical Association 77: 177–183.
Robinson, D. O.; Hammans, S. R. (2005) “Oculopharyngeal muscular dystrophy (OPMD): analysis of the PABPN1 gene expansion sequence in 86 patients reveals 13 different expansion types and further evidence for unequal recombination as the mutational mechanism”, Hum Genet 116(4): 267–271.
Rodríguez, L.; Casas, G.; Grau, R.; Pupo, M. (2006) “Scan Statistics. Bioinformatics Applications”, First International WorkShop on Bioinformatics Cuba-Flanders’2006, Universidad Central de Las Villas, Santa Clara, Febrero.
Rodríguez, L.; Casas, G.; Grau, R. (2006) “Aplicación de los métodos Scan en Bioinformática” Uciencia 2006. II Conferencia Científica, UCI, La Habana, Julio.
Volfovsky, N., Haas, B.J. (2001) “A clustering method for repeat analysis in DNA sequences”, Genome Biology 2(8)