By a News Reporter-Staff News Editor at Information Technology Newsweekly -- New research on Bioinformatics is the subject of a report. According to news reporting originating in San Antonio, Texas, by VerticalNews journalists, research stated, "Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene's expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network."
The news reporters obtained a quote from the research from the University of Texas Health Science Center, "The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments. We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient 'over-shrinkage' method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods."
According to the news reporters, the research concluded: "By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved."
For more information on this research see: Differential expression analysis with global network adjustment. Bmc Bioinformatics, 2013;14():258. (BioMed Central - www.biomedcentral.com/; Bmc Bioinformatics - www.biomedcentral.com/bmcbioinformatics/)
Our news correspondents report that additional information may be obtained by contacting J.A. Gelfond, Dept. of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, Texas, United States. Additional authors for this research include J.G. Ibrahim, M. Gupta, M.H. Chen and J.D Cody.
Keywords for this news article include: Texas, San Antonio, United States, Bioinformatics, North and Central America.
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