Gene ontology semi-supervised possibilistic clustering of gene expression data

Ioannis A. Maraziotis, George Dimitrakopoulos, Anastasios Bezerianos

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

Abstract

Clustering is one of the most important data analysis methods with applications of significant importance in many scientific fields. In computational biology, clustering of gene expression data from microarrays assists biologists to investigate uncharacterized genes by identifying biologically relevant groups of genes. Semi-supervised clustering algorithms have proven to bring substantial improvements in the results of standard clustering methods especially on datasets of increased complexity. In this paper we propose a semi-supervised possibilistic clustering algorithm (SSPCA) utilizing supervision via pair-wise constraints indicating whether a pair of patterns should belong to the same cluster or not. Furthermore we show how external sources of biological information like gene ontology data can provide constraints to guide the clustering process of SSPCA. Our results show that the proposed algorithm outperformed other well established standard and semi-supervised methodologies.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence
Subtitle of host publicationTheories and Applications - 7th Hellenic Conference on AI, SETN 2012, Proceedings
Pages262-269
Number of pages8
DOIs
StatePublished - Jun 5 2012
Event7th Hellenic Conference on Artificial Intelligence, SETN 2012 - Lamia, Greece
Duration: May 28 2012May 31 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7297 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th Hellenic Conference on Artificial Intelligence, SETN 2012
CountryGreece
CityLamia
Period5/28/125/31/12

Keywords

  • constraints
  • gene expression
  • gene ontology
  • possibilistic clustering
  • semi-supervision

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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