Ponencia
Classification of Gene Expression Profiles: Comparison of K-means and Expectation Maximization Algorithms
Autor/es | Rubio Escudero, Cristina
Martínez Álvarez, Francisco Romero Zaliz, Rocío Zwir, Igor |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2008 |
Fecha de depósito | 2022-11-30 |
Publicado en |
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ISBN/ISSN | 978-0-7695-3326-1 |
Resumen | Biomedical research has been revolutionized by high throughput techniques and the enormous amount of data
they are able to generate. In particular technology has the
capacity to monitor changes in RNA abundance for thou ... Biomedical research has been revolutionized by high throughput techniques and the enormous amount of data they are able to generate. In particular technology has the capacity to monitor changes in RNA abundance for thou sands of genes simultaneously. The interest shown over microarray analysis methods has rapidly raised. Clustering is widely used in the analysis of microarray data to group genes of interest targeted from microarray experiments on the basis of similarity of expression patterns. In this work we apply two clustering algorithms, K-means and Expecta tion Maximization to particular a problem and we compare the groupings obtained on the basis of the cohesiveness of the gene products associated to the genes in each cluster |
Agencias financiadoras | Ministerio de Ciencia Y Tecnología (MCYT). España Junta de Andalucía |
Identificador del proyecto | TIN-2006-12879
TIC-02788 |
Cita | Rubio Escudero, C., Martínez Álvarez, F., Romero Zaliz, R. y Zwir, I. (2008). Classification of Gene Expression Profiles: Comparison of K-means and Expectation Maximization Algorithms. En HIS 2008: 8th International Conference on Hybrid Intelligent Systems (831-836), Barcelona, España: IEEE Computer Society. |
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