Clustering of gene expression profiles applied to marine research

Vanessa Aguiar-Pulido, Victoria Suárez-Ulloa, Daniel Rivero, José M. Eirín-López, Julián Dorado

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

1 Scopus citations

Abstract

This work presents the results of applying two clustering techniques to gene expression data from 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 (SOM) and the k-means algorithm were used, partitioning the input data into nine clusters. The resulting clusters were then analysed using Gene Ontology (GO) data, obtaining results that suggest that SOM clusters could be more homogeneous than those obtained by the k-means technique.

Original languageEnglish (US)
Title of host publicationAdvances in Computational Intelligence - 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings
Pages453-462
Number of pages10
EditionPART 1
DOIs
StatePublished - 2013
Externally publishedYes
Event12th International Work-Conference on Artificial Neural Networks, IWANN 2013 - Puerto de la Cruz, Tenerife, Spain
Duration: Jun 12 2013Jun 14 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Work-Conference on Artificial Neural Networks, IWANN 2013
Country/TerritorySpain
CityPuerto de la Cruz, Tenerife
Period6/12/136/14/13

Keywords

  • Bioinformatics
  • Clustering
  • Data mining
  • Gene ontology
  • Microarray
  • Neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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