MicroCluster

Efficient deterministic biclustering of microarray data

Lizhuang Zhao, Mohammed J. Zaki

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

MicroCluster, an efficient deterministic biclustering of microarray data, was described. MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. MicroCluster can delete or merge biclusters that have large overlaps. A set of metrics has been developed to evaluate the clustering quality and to test MicroCluster's effectiveness on several synthetic and real data sets. MicroCluster's merging and deletion stages control the noise tolerance in a cluster appropriately. It was also checked whether if any clusters that MicroCluster discovered share a common gene process, function, or cellular location, using the Gene Ontology project data.

Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalIEEE Intelligent Systems
Volume20
Issue number6
DOIs
Publication statusPublished - 1 Nov 2005
Externally publishedYes

Fingerprint

Microarrays
Genes
Merging
Ontology

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

MicroCluster : Efficient deterministic biclustering of microarray data. / Zhao, Lizhuang; Zaki, Mohammed J.

In: IEEE Intelligent Systems, Vol. 20, No. 6, 01.11.2005, p. 40-49.

Research output: Contribution to journalArticle

Zhao, Lizhuang ; Zaki, Mohammed J. / MicroCluster : Efficient deterministic biclustering of microarray data. In: IEEE Intelligent Systems. 2005 ; Vol. 20, No. 6. pp. 40-49.
@article{711fb0fc53fd40f88052d3bdf1d3f413,
title = "MicroCluster: Efficient deterministic biclustering of microarray data",
abstract = "MicroCluster, an efficient deterministic biclustering of microarray data, was described. MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. MicroCluster can delete or merge biclusters that have large overlaps. A set of metrics has been developed to evaluate the clustering quality and to test MicroCluster's effectiveness on several synthetic and real data sets. MicroCluster's merging and deletion stages control the noise tolerance in a cluster appropriately. It was also checked whether if any clusters that MicroCluster discovered share a common gene process, function, or cellular location, using the Gene Ontology project data.",
author = "Lizhuang Zhao and Zaki, {Mohammed J.}",
year = "2005",
month = "11",
day = "1",
doi = "10.1016/j.matlet.2004.09.018",
language = "English",
volume = "20",
pages = "40--49",
journal = "IEEE Intelligent Systems and Their Applications",
issn = "1541-1672",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",

}

TY - JOUR

T1 - MicroCluster

T2 - Efficient deterministic biclustering of microarray data

AU - Zhao, Lizhuang

AU - Zaki, Mohammed J.

PY - 2005/11/1

Y1 - 2005/11/1

N2 - MicroCluster, an efficient deterministic biclustering of microarray data, was described. MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. MicroCluster can delete or merge biclusters that have large overlaps. A set of metrics has been developed to evaluate the clustering quality and to test MicroCluster's effectiveness on several synthetic and real data sets. MicroCluster's merging and deletion stages control the noise tolerance in a cluster appropriately. It was also checked whether if any clusters that MicroCluster discovered share a common gene process, function, or cellular location, using the Gene Ontology project data.

AB - MicroCluster, an efficient deterministic biclustering of microarray data, was described. MicroCluster can mine different types of arbitrarily positioned and overlapping clusters of genetic data to find interesting patterns. MicroCluster can delete or merge biclusters that have large overlaps. A set of metrics has been developed to evaluate the clustering quality and to test MicroCluster's effectiveness on several synthetic and real data sets. MicroCluster's merging and deletion stages control the noise tolerance in a cluster appropriately. It was also checked whether if any clusters that MicroCluster discovered share a common gene process, function, or cellular location, using the Gene Ontology project data.

UR - http://www.scopus.com/inward/record.url?scp=30344442111&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=30344442111&partnerID=8YFLogxK

U2 - 10.1016/j.matlet.2004.09.018

DO - 10.1016/j.matlet.2004.09.018

M3 - Article

VL - 20

SP - 40

EP - 49

JO - IEEE Intelligent Systems and Their Applications

JF - IEEE Intelligent Systems and Their Applications

SN - 1541-1672

IS - 6

ER -