Semi supervised fuzzy clustering networks for constrained analysis of time-series gene expression data

Ioannis A. Maraziotis, Andrei Dragomir, Anastasios Bezerianos

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

Abstract

Clustering analysis of time series data from DNA microarray hybridization studies is essential for identifying biological relevant groups of genes. Microarrrays provide large datasets that are currently primarily analyzed using crisp clustering techniques. Crisp clustering methods such as K-means or self organizing maps assign each gene to one cluster, thus omitting information concerning the multiple roles of genes. One of the major advantages of fuzzy clustering is that genes can belong to more than one group, revealing this way more profound information concerning the function and regulation of each gene. Additionally, recent studies have proven that integrating a small amount of information in purely unsupervised algorithms leads to much better performance. In this paper we propose a new semi-supervised fuzzy clustering algorithm which we apply in time series gene expression data. The clustering that was performed on simulated as well as experimental microarray data proved that the proposed method outperformed other clustering techniques.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Pages818-826
Number of pages9
ISBN (Print)3540388710, 9783540388715
DOIs
StatePublished - Jan 1 2006
Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
Duration: Sep 10 2006Sep 14 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4132 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Artificial Neural Networks, ICANN 2006
CountryGreece
CityAthens
Period9/10/069/14/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Maraziotis, I. A., Dragomir, A., & Bezerianos, A. (2006). Semi supervised fuzzy clustering networks for constrained analysis of time-series gene expression data. In Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings (pp. 818-826). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4132 LNCS - II). Springer Verlag. https://doi.org/10.1007/11840930_85