Mining biologically significant co-regulation patterns from microarray data

Yuhai Zhao*, Ying Yin, Guoren Wang

*Corresponding author for this work

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

Abstract

In this paper, we propose a novel model, namely g-Cluster, to mine biologically significant co-regulated gene clusters. The proposed model can (1) discover extra co-expressed genes that cannot be found by current pattern/tendency-based methods, and (2) discover inverted relationship overlooked by pattern/tendency-based methods. We also design two tree-based algorithms to mine all qualified g-Clusters. The experimental results show: (1) our approaches are effective and efficient, and (2) our approaches can find an amount of co-regulated gene clusters missed by previous models, which are potentially of high biological significance.

Original languageEnglish
Title of host publicationRough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings
PublisherSpringer Verlag
Pages408-414
Number of pages7
ISBN (Print)3540362975, 9783540362975
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventFirst International Conference on Rough Sets and Knowledge Technology, RSKT 2006 - Chongqing, China
Duration: 24 Jul 200626 Jul 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4062 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceFirst International Conference on Rough Sets and Knowledge Technology, RSKT 2006
Country/TerritoryChina
CityChongqing
Period24/07/0626/07/06

Keywords

  • Bioinformatics
  • Clustering
  • Micro-array data

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