Cluster analysis of gene expression profiles using automatically extracted seeds

Miyoung Shin, Seon Hee Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper addresses the problem of clustering gene expression profiles based on automatically extracted seeds which are obtained by our proposed method. Specifically, we introduce a new clustering methodology that consists of three stages: seed extraction, cluster generation, and its evaluation. Performance analysis of the proposed methodology is done with a real dataset, and its results are reported in detail. Overall, based on our empirical studies, the proposed clustering methodology seems to be very favorable for gene expression data analysis, as alternatives to current clustering methods.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Verlag
Pages263-269
Number of pages7
ISBN (Print)9783540232056
DOIs
StatePublished - 2004
Event8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004 - Wellington, New Zealand
Duration: 20 Sep 200425 Sep 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3215
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004
Country/TerritoryNew Zealand
CityWellington
Period20/09/0425/09/04

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