A novel data mining algorithm for reconstructing gene regulatory networks from microarray data

Patrick C.H. Ma, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In this paper, we propose a novel data mining algorithm for reconstructing gene regulatory networks (GRNs) from microarray data. By making use of the proposed probabilistic measure, it is able to mine noisy, high dimensional expression data for interesting association patterns of genes without the need for additional feature selection procedures. Moreover, it can make explicit hidden patterns discovered for possible biological interpretation and also predict gene expression patterns in the unseen tissue samples. Experimental results on real expression data show that it is very effective and the discovered association patterns reveal biologically meaningful regulatory relationships of genes that could help users reconstructing the underlying structures of GRNs.
Original languageEnglish
Title of host publicationApplied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing
Pages202-203
Number of pages2
Volume1
Publication statusPublished - 21 Nov 2006
Event2006 ACM Symposium on Applied Computing - Dijon, France
Duration: 23 Apr 200627 Apr 2006

Conference

Conference2006 ACM Symposium on Applied Computing
CountryFrance
CityDijon
Period23/04/0627/04/06

Keywords

  • Bioinformatics
  • Data mining
  • Gene regulatory networks (GRNs)

ASJC Scopus subject areas

  • Software

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