Classical expression model of entangled quantum microwave after the measurement

2019 ◽  
Vol 55 (8) ◽  
pp. 473-475
Author(s):  
Xiang Li ◽  
De‐Wei Wu ◽  
Qiang Miao ◽  
Jun‐Wen Luo
Keyword(s):  
Classical Expression ◽  
Expression Model

TWO DIFFERENT APPROACHES FOR GENE EXPRESSION MODEL CONTROL

BIOMAT 2011 ◽  
2012 ◽  
pp. 153-177
Author(s):  
N. A. BARBOSA ◽  
H DÍAZ ◽  
A. RAMIREZ
Keyword(s):  
Gene Expression ◽  
Model Control ◽  
Expression Model ◽  
Gene Expression Model

scholarly journals Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1531
Author(s):  
Vânia Tavares ◽  
Joana Monteiro ◽  
Evangelos Vassos ◽  
Jonathan Coleman ◽  
Diana Prata
Keyword(s):  
Gene Expression ◽  
Frontal Cortex ◽  
Model Performance ◽  
Variation Method ◽  
Expression Model ◽  
The Brain ◽  
Gene Expression Model ◽  
And Training ◽  
Predict Gene Expression ◽  
Fold Cross Validation

Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC.

scholarly journals Heterogeneous recruitment abilities to RNA polymerases generate nonlinear scaling of gene expression with cell volume

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qirun Wang ◽  
Jie Lin
Keyword(s):  
Gene Expression ◽  
Cell Volume ◽  
Direct Consequence ◽  
Rna Polymerases ◽  
Mrna Levels ◽  
Expression Levels ◽  
Promoter Sequences ◽  
Biophysical Mechanism ◽  
Expression Model ◽  
Cycle Regulation

AbstractWhile most genes’ expression levels are proportional to cell volumes, some genes exhibit nonlinear scaling between their expression levels and cell volume. Therefore, their mRNA and protein concentrations change as the cell volume increases, which often have crucial biological functions such as cell-cycle regulation. However, the biophysical mechanism underlying the nonlinear scaling between gene expression and cell volume is still unclear. In this work, we show that the nonlinear scaling is a direct consequence of the heterogeneous recruitment abilities of promoters to RNA polymerases based on a gene expression model at the whole-cell level. Those genes with weaker (stronger) recruitment abilities than the average ability spontaneously exhibit superlinear (sublinear) scaling with cell volume. Analysis of the promoter sequences and the nonlinear scaling of Saccharomyces cerevisiae’s mRNA levels shows that motifs associated with transcription regulation are indeed enriched in genes exhibiting nonlinear scaling, in concert with our model.

scholarly journals P3-066: A Cellular Expression Model of TAU Hyperphosphorylation and Aggregation That does not Utilize Frontotemporal Dementia Mutations

2016 ◽  
Vol 12 ◽  
pp. P842-P843
Author(s):  
Joel B. Schachter ◽  
Mali Cosden ◽  
Jeffrey Meteer ◽  
John Majercak ◽  
Fred Hess ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Tau Hyperphosphorylation ◽  
Cellular Expression ◽  
Expression Model
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