• Aguiar Pulido, Vanessa
• Suárez-Ulloa, Victoria
• Rivero Cebrián, Daniel
• Eirín-López, José M.
• Dorado de la Calle, Julián

This work presents the results of applyng two clustering techniques to gene expression data form the mussel Mytilus galloprovincialis. the objective of the study presented in this paper was to cluster the different genes involved in the experiment, in order to find those most closely related based on their expression patterns. A self-organising map COM and the k-means algorithm were used, partitionaing the imput data into nine clusters. The resultin clusters were the analysed using Gene Ontonlogy data, obtainging results that suggest that SOM clusters could be more homogeneous tan those obtained by the k-means technique.