ArticleStatistics in medicine2010
A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data.
Article in Statistics in medicine, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Bayesian hidden Markov models to identify RNA-protein interaction sites in PAR-CLIP.Biometrics · 2014Article
- Detection of candidate tumor driver genes using a fully integrated Bayesian approach.Statistics in medicine · 2014Article
- A graphical model method for integrating multiple sources of genome-scale data.Statistical applications in genetics and molecular biology · 2013Article
- Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data.BMC systems biology · 2013Article
- Bayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription Factor.The annals of applied statistics · 2012Article
- Statistical methods for integrating multiple types of high-throughput data.Methods in molecular biology (Clifton, N.J.) · 2010Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The genome-wide DNA-protein-binding data, DNA sequence data and gene expression data represent complementary means to deciphering global and local transcriptional regulatory circuits. Combining these different types of data can not only improve the statistical power, but also provide a more comprehensive picture of gene regulation. In this paper, we propose a novel statistical model to augment protein-DNA-binding data with gene expression and DNA sequence data when available. We specify a hierarchical Bayes model and use Markov chain Monte Carlo simulations to draw inferences. Both simulation studies and an analysis of an experimental data set show that the proposed joint modeling method can significantly improve the specificity and sensitivity of identifying target genes as compared with conventional approaches relying on a single data source.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.