30 research outputs found
Comparison of means of the EC distributions for human-mouse 1-1 orthologs based on the whole microarray data with the expression data over 26 common tissues by using co-expression based methods.
<p>Comparison of means of the EC distributions for human-mouse 1-1 orthologs based on the whole microarray data with the expression data over 26 common tissues by using co-expression based methods.</p
Comparison of the EC distributions for (a) human-mouse random gene pairs and (b) human-mouse 1-1 orthologs using Liao and Zhang's method (L), Dutilh et al.'s method (D), ICC and Essien et al.'s method (E).
<p>Comparison of the EC distributions for (a) human-mouse random gene pairs and (b) human-mouse 1-1 orthologs using Liao and Zhang's method (L), Dutilh et al.'s method (D), ICC and Essien et al.'s method (E).</p
Means and standard deviations of the EC distributions generated by different methods.
<p>Means and standard deviations of the EC distributions generated by different methods.</p
A computational framework for predicting obesity risk based on optimizing and integrating genetic risk score and gene expression profiles
<div><p>Recent large-scale genome-wide association studies have identified tens of genetic loci robustly associated with Body Mass Index (BMI). Gene expression profiles were also found to be associated with BMI. However, accurate prediction of obesity risk utilizing genetic data remains challenging. In a cohort of 75 individuals, we integrated 27 BMI-associated SNPs and obesity-associated gene expression profiles. Genetic risk score was computed by adding BMI-increasing alleles. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used that excluded some SNPs. Linear regression and support vector machine models were built to predict obesity risk using gene expression profiles and the genetic risk score. An adjusted R<sup>2</sup> of 0.556 and accuracy of 76% was achieved for the linear regression and support vector machine models, respectively. In this paper, we report a new mathematical method to predict obesity genetic risk. We constructed obesity prediction models based on genetic information for a small cohort. Our computational framework serves as an example for using genetic information to predict obesity risk for specific cohorts.</p></div
Multiomic/Phenotype concordance of the 5 SNPs.
<p>Multiomic/Phenotype concordance of the 5 SNPs.</p
Flow charts for the computational procedures.
<p>A) Flow chart of the entire procedure of data processing and analysis. B) Flow chart of the feature selection algorithm for SNP data. C) Flow chart of the feature selection algorithm for microarray data.</p
Baseline demographic characteristics of the 90 participants with BMI data.
<p>Baseline demographic characteristics of the 90 participants with BMI data.</p
Correlations between genetic risk score and BMI-associated SNPs.
<p>Correlations between genetic risk score and BMI-associated SNPs.</p
Comparison of Ks distributions among WGD, local and dispersed duplicates in the investigated core eudicot genomes.
<p>For <i>Glycine max</i>, <i>Solanum tuberosum</i> and <i>Malus x domestica</i> whose genomes are large and recent duplicates are rampant, a second plot is provided to show the right tails of Ks distributions.</p
Functional comparison of different origins of duplicates.
<p>(A) Clustering of functional profiles. (B) Comparison of fold enrichment for essential genes. (C) Comparison of fold enrichment for the genes involved in protein-protein interactions (PPIs).</p