Classification of gene expression profiles: comparison of K-means and expectation maximization algorithms

Cristina Rubio-Escudero, Francisco Martínez-Álvarez, Rocío Romero-Zaliz, Igor Zwir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

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 thousands 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 Expectation 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.

Original languageEnglish
Title of host publicationProceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008
Pages831-836
Number of pages6
DOIs
StatePublished - Nov 10 2008
Externally publishedYes
Event8th International Conference on Hybrid Intelligent Systems, HIS 2008 - Barcelona, Spain
Duration: Sep 10 2008Sep 12 2008

Publication series

NameProceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008

Conference

Conference8th International Conference on Hybrid Intelligent Systems, HIS 2008
CountrySpain
CityBarcelona
Period09/10/0809/12/08

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    Rubio-Escudero, C., Martínez-Álvarez, F., Romero-Zaliz, R., & Zwir, I. (2008). Classification of gene expression profiles: comparison of K-means and expectation maximization algorithms. In Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008 (pp. 831-836). [4626734] (Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008). https://doi.org/10.1109/HIS.2008.92