Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis

Li Chen; Jianhua Xuan; Yue Wang; Eric P. Hoffman; Rebecca B. Riggins; Robert Clarke

doi:10.1504/IJCBDD.2009.027582

Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis

Li Chen, Jianhua Xuan, Yue Wang, Eric P. Hoffman, Rebecca B. Riggins, Robert Clarke

Research output: Contribution to journal › Article › peer-review

Abstract

Many computational methods for identification of transcription regulatory modules often result in many false positives in practice due to noise sources of binding information and gene expression profiling data. In this paper, we propose a multi-level strategy for condition-specific gene regulatory module identification by integrating motif binding information and gene expression data through support vector regression and significant analysis. We have demonstrated the feasibility of the proposed method on a yeast cell cycle data set. The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer.

Original language	English (US)
Pages (from-to)	1-20
Number of pages	20
Journal	International Journal of Computational Biology and Drug Design
Volume	2
Issue number	1
DOIs	https://doi.org/10.1504/IJCBDD.2009.027582
State	Published - Aug 2009
Externally published	Yes

Keywords

Motif enrichment analysis
Multi-level regulator identification
SVR
Statistical significance analysis
Support vector regression
Transcription regulatory module

ASJC Scopus subject areas

Drug Discovery
Computer Science Applications

Access to Document

10.1504/IJCBDD.2009.027582

Cite this

@article{81ee8ae05e1e4801bf4ff8cd681ddca9,

title = "Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis",

abstract = "Many computational methods for identification of transcription regulatory modules often result in many false positives in practice due to noise sources of binding information and gene expression profiling data. In this paper, we propose a multi-level strategy for condition-specific gene regulatory module identification by integrating motif binding information and gene expression data through support vector regression and significant analysis. We have demonstrated the feasibility of the proposed method on a yeast cell cycle data set. The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer.",

keywords = "Motif enrichment analysis, Multi-level regulator identification, SVR, Statistical significance analysis, Support vector regression, Transcription regulatory module",

author = "Li Chen and Jianhua Xuan and Yue Wang and Hoffman, {Eric P.} and Riggins, {Rebecca B.} and Robert Clarke",

year = "2009",

month = aug,

doi = "10.1504/IJCBDD.2009.027582",

language = "English (US)",

volume = "2",

pages = "1--20",

journal = "International Journal of Computational Biology and Drug Design",

issn = "1756-0756",

publisher = "Inderscience Enterprises Ltd",

number = "1",

}

TY - JOUR

T1 - Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis

AU - Chen, Li

AU - Xuan, Jianhua

AU - Wang, Yue

AU - Hoffman, Eric P.

AU - Riggins, Rebecca B.

AU - Clarke, Robert

PY - 2009/8

Y1 - 2009/8

N2 - Many computational methods for identification of transcription regulatory modules often result in many false positives in practice due to noise sources of binding information and gene expression profiling data. In this paper, we propose a multi-level strategy for condition-specific gene regulatory module identification by integrating motif binding information and gene expression data through support vector regression and significant analysis. We have demonstrated the feasibility of the proposed method on a yeast cell cycle data set. The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer.

AB - Many computational methods for identification of transcription regulatory modules often result in many false positives in practice due to noise sources of binding information and gene expression profiling data. In this paper, we propose a multi-level strategy for condition-specific gene regulatory module identification by integrating motif binding information and gene expression data through support vector regression and significant analysis. We have demonstrated the feasibility of the proposed method on a yeast cell cycle data set. The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer.

KW - Motif enrichment analysis

KW - Multi-level regulator identification

KW - SVR

KW - Statistical significance analysis

KW - Support vector regression

KW - Transcription regulatory module

UR - http://www.scopus.com/inward/record.url?scp=77953675402&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953675402&partnerID=8YFLogxK

U2 - 10.1504/IJCBDD.2009.027582

DO - 10.1504/IJCBDD.2009.027582

M3 - Article

C2 - 20054984

AN - SCOPUS:77953675402

SN - 1756-0756

VL - 2

SP - 1

EP - 20

JO - International Journal of Computational Biology and Drug Design

JF - International Journal of Computational Biology and Drug Design

IS - 1

ER -

Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this