CAGER: classification analysis of gene expression regulation using multiple information sources

CAGER: classification analysis of gene expression regulation using multiple information sources

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by Jianhua Ruan , Weixiong Zhang

Venue:	BMC BIOINFORMATICS
Citations:	2 - 1 self

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Datum	Value	Source
TITLE	CAGER: classification analysis of gene expression regulation using multiple information sources	user correction
AUTHOR NAME	Jianhua Ruan	user correction
AUTHOR AFFIL	2Department of Genetics, Washington University School of Medicine	user correction
AUTHOR ADDR	660 S. Euclid Ave., St Louis, MO 63110, USA	user correction
AUTHOR NAME	Weixiong Zhang	user correction
AUTHOR AFFIL	2Department of Genetics, Washington University School of Medicine	user correction
AUTHOR ADDR	660 S. Euclid Ave., St Louis, MO 63110, USA	user correction
ABSTRACT	Background: Many classification approaches have been applied to analyzing transcriptional regulation of gene expressions. These methods build models that can explain a gene’s expression level from the regulatory elements (features) on its promoter sequence. Different types of features, such as experimentally verified binding motifs, motifs discovered by computer programs, or transcription factor binding data measured with Chromatin Immuno-precipitation (ChIP) assays, have been used towards this goal. Each type of features has been shown successful in modeling gene transcriptional regulation under certain conditions. However, no comparison has been made to evaluate the relative merit of these features. Furthermore, most publicly available classification tools were not designed specifically for modeling transcriptional regulation, and do not allow the user to combine different types of features. Results: In this study, we use a specific classification method, decision trees, to model transcriptional regulation in yeast with features based on predefined motifs, automatically identified motifs, ChIP-chip data, or their com-binations. We compare the accuracies and stability of these models, and analyze their capabilities in identifying functionally related genes. Furthermore, we design and implement a user-friendly web server called CAGER (Clas-sification Analysis of Gene Expression Regulation) that integrates several software components for automated	SVM HeaderParse 0.2
VENUE	BMC BIOINFORMATICS	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	114	INFERENCE
VOLUME	6	INFERENCE
CITATIONS	63 found	ParsCit 1.0