Setting a generalized functional linear model (GFLM) for the classification of different types of cancer
Título del documento: Setting a generalized functional linear model (GFLM) for the classification of different types of cancer
Revista: Latin-American Journal of Computing (LAJC)
Base de datos:
Número de sistema: 000565069
ISSN: 1390-9134
Autores: 1
2
3
Instituciones: 1Escuela Politécnica Nacional, Departamento de Matemáticas, Quito, Pichincha. Ecuador
2Universidad de las Américas, Quito, Pichincha. Ecuador
3Universidad de las Fuerzas Armadas, Sangolquí, Pichincha. Ecuador
Año:
Volumen: 3
Número: 2
Paginación: 8-8
País: Ecuador
Idioma: Inglés
Tipo de documento: Artículo
Resumen en inglés This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005) and Ferraty et al. (2006).
Disciplinas: Ciencias de la computación,
Ciencias de la computación
Palabras clave: Inteligencia artificial,
Procesamiento de datos
Keyword: Artificial intelligence,
Data processing
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