NDLI: Bayesian variable selection for gene expression modeling with regulatory motif binding sites in neuroinflammatory events

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Bayesian variable selection for gene expression modeling with regulatory motif binding sites in neuroinflammatory events

Content Provider	Springer Nature Link
Author	Liu, Kuang Yu Zhou, Xiaobo Kan, Kinhong Wong, Stephen T. C.
Copyright Year	2006
Abstract	Multiple transcription factors (TFs) coordinately control transcriptional regulation of genes in eukaryotes. Although numerous computational methods focus on the identification of individual TF-binding sites (TFBSs), very few consider the interdependence among these sites. In this article, we studied the relationship between TFBSs and microarray gene expression levels using both family-wise and member-specific motifs, under various combination of regression models with Bayesian variable selection, as well as motif scoring and sharing conditions, in order to account for the coordination complexity of transcription regulation. We proposed a three-step approach to model the relationship. In the first step, we preprocessed microarray data and usedp-values and expression ratios to preselect upregulated and down-regulated genes. The second step aimed to identify and score individual TFBSs within DNA sequence of each gene. A method based on the degree of similarity and the number of TFBSs was employed to calculate the score of each TFBS in each gene sequence. In the last step, linear regression and probit regression were used to build a predictive model of gene expression outcomes using these TFBSs as predictors. Given a certain number of predictors to be used, a full search of all possible predictor sets is usually combinatorially prohibitive. Therefore, this article considered the Bayesian variable selection for prediction using either of the regression models. The Bayesian variable selection applied in the context of gene selection, missing value estimation, and regulatory motif identification. In our modeling, the regressor was approximated as a linear combination of the TFBSs and a Gibbs sampler was employed to find the strongest TFBSs. We applied these regression models with the Bayesian variable selection on spinal cord injury gene expression data set. These TFs demonstrated intricate regulatory roles either as a family or as individual members in neuroinflammatory events. Our an alysis can be applied to create plausible hypotheses for combinatorial regulation by TFBSs and avoiding false-positive candidates in the modeling process at the same time. Such a systematic approach provides the possibility to dissect transcription regulation, from a more comprehensive perspective, through which phenotypical events at cellular and tissue levels are moved forward by molecular events at gene transcription and translation levels.
Starting Page	95
Ending Page	117
Page Count	23
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	4
Issue Number	1
e-ISSN	15590089
Language	English
Publisher	Humana Press
Publisher Date	2006-01-01
Publisher Place	Totowa
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Biotechnology Engineering Neurology
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

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