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

Patrick C.H. Ma; Chun Chung Chan

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 proceeding › Conference article published in proceeding or book › Academic research › peer-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 language	English
Title of host publication	Applied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing
Pages	202-203
Number of pages	2
Volume	1
Publication status	Published - 21 Nov 2006
Event	2006 ACM Symposium on Applied Computing - Dijon, France Duration: 23 Apr 2006 → 27 Apr 2006

Conference

Conference	2006 ACM Symposium on Applied Computing
Country/Territory	France
City	Dijon
Period	23/04/06 → 27/04/06

Keywords

Bioinformatics
Data mining
Gene regulatory networks (GRNs)

ASJC Scopus subject areas

Software

Cite this

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title = "A novel data mining algorithm for reconstructing gene regulatory networks from microarray data",

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.",

keywords = "Bioinformatics, Data mining, Gene regulatory networks (GRNs)",

author = "Ma, {Patrick C.H.} and Chan, {Chun Chung}",

year = "2006",

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language = "English",

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volume = "1",

pages = "202--203",

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note = "2006 ACM Symposium on Applied Computing ; Conference date: 23-04-2006 Through 27-04-2006",

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A novel data mining algorithm for reconstructing gene regulatory networks from microarray data. / Ma, Patrick C.H.; Chan, Chun Chung.
Applied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing. Vol. 1 2006. p. 202-203.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - 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.

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KW - Data mining

KW - Gene regulatory networks (GRNs)

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BT - Applied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing

T2 - 2006 ACM Symposium on Applied Computing

Y2 - 23 April 2006 through 27 April 2006

ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

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