Finding Similar Patterns in Microarray Data

Ziangsheng Chen; Jiuyong Li; Grant Daggard; Xiaodi Huang

doi:10.1007/11589990_185

Finding Similar Patterns in Microarray Data

Ziangsheng Chen, Jiuyong Li, Grant Daggard, Xiaodi Huang

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable.

Original language	English
Title of host publication	Al 2005
Subtitle of host publication	advances in artificial intelligence. 18th Australian Joint Conference on Artificial Intelligence
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	1272-1276
Number of pages	5
DOIs	https://doi.org/10.1007/11589990_185
Publication status	Published - 2005
Event	Australian Joint Conference on Artificial Intelligence - Sydney, Australia, Australia Duration: 05 Dec 2005 → 09 Dec 2005

Conference

Conference	Australian Joint Conference on Artificial Intelligence
Country/Territory	Australia
Period	05/12/05 → 09/12/05

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10.1007/11589990_185

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@inproceedings{3966c914b850402695446ccc4e8626f7,

title = "Finding Similar Patterns in Microarray Data",

abstract = "In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable.",

author = "Ziangsheng Chen and Jiuyong Li and Grant Daggard and Xiaodi Huang",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = Berlin, Germany: Springer, 2005. Event dates (773o) = 5-9 December 2005; Parent title (773t) = Australian Joint Conference on Artificial Intelligence. ISSNs: 0302-9743; ; Australian Joint Conference on Artificial Intelligence ; Conference date: 05-12-2005 Through 09-12-2005",

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doi = "10.1007/11589990_185",

language = "English",

pages = "1272--1276",

booktitle = "Al 2005",

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TY - GEN

T1 - Finding Similar Patterns in Microarray Data

AU - Chen, Ziangsheng

AU - Li, Jiuyong

AU - Daggard, Grant

AU - Huang, Xiaodi

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = Berlin, Germany: Springer, 2005. Event dates (773o) = 5-9 December 2005; Parent title (773t) = Australian Joint Conference on Artificial Intelligence. ISSNs: 0302-9743;

PY - 2005

Y1 - 2005

N2 - In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable.

AB - In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable.

U2 - 10.1007/11589990_185

DO - 10.1007/11589990_185

M3 - Conference paper

SP - 1272

EP - 1276

BT - Al 2005

PB - Springer

CY - Berlin, Germany

T2 - Australian Joint Conference on Artificial Intelligence

Y2 - 5 December 2005 through 9 December 2005

ER -

Finding Similar Patterns in Microarray Data

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