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Identifying transcription factor targets using enhanced Bayesian classifier

Paper ID Volume ID Publish Year Pages File Format Full-Text
15542 1424 2007 6 PDF Available
Title
Identifying transcription factor targets using enhanced Bayesian classifier
Abstract

Finding transcription factors (TFs) to their target genes (TGs) is the first step to understand the transcriptional regulatory networks. Here we present a method which uses an enhanced Bayesian classifier to predict the TF–TG pairs in time-course expression data. Different from previous prediction models, the gene expression data is encoded by discrete values and the temporal feature is used in the enhanced Bayesian classifier. The enhanced Bayesian classifier is trained and tested on two groups of positive and negative samples by three-fold cross-validation and compared with other methods. As the prediction result is improved obviously, the enhanced Bayesian classifier represents a new perspective on studying the regulation relationships from gene expression data. Further more, a data selection method which focus on ‘active’ TFs is proposed, suggesting a new approach on selecting effective time-course expression data.

Keywords
Time-course expression data; Transcription factor targets; Bayesian classifier
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 31, Issues 5–6, October 2007, Pages 355–360
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us