Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data

<div><p>Gene expression is perturbated by drugs to different extent. Analyzing genes whose expression is frequently regulated by drugs would be useful for the screening of candidate therapeutic targets and genes implicated in side effect. Here, we obtained the differential expression number (DEN) for genes profiled in Affymetrix microarrays from the Connectivity Map project, and conducted systemic comparative computational analysis between high DEN genes and other genes. Results indicated that genes with higher down-/up-regulation number (down_h/up_h) tended to be clustered in genome, and have lower homologous gene number, higher SNP density and more disease-related SNP. Down_h and up_h were significantly enriched in cancer related pathways, while genes with lower down-/up-regulation number (down_l/up_l) were mainly involved in the development of nervous system diseases. Besides, up_h had lower interaction network degree, later developmental stage to express, higher tissue expression specificity than up_l, while down_h showed reversed tendency in comparison with down_l. Together, our analysis suggests that genes frequently regulated by drugs are more likely to be associated with disease-related functions, but the extensive activation of conserved and widely expressed genes by drugs is disfavored.</p></div

Machine Learning Assisted Characterization of Local Bubble Properties and Its Coupling with the EMMS Bubbling Drag

Empirical correlations for bubble diameter and velocity are incapable of predicting the local bubble behaviors fairly because the impact of local hydrodynamics on bubbles in fluidized beds. Based on image processing, a novel bubble identification method with an adaptive threshold was proposed to distinguish and characterize bubbles in fluidized beds. The information regarding bubble properties and local hydrodynamics can thus be extracted using the big data from highly resolved simulations. Accordingly, the deep neural network was trained to accurately predict local bubble properties, where the inputs were determined by performing correlation analysis and a random forest algorithm. We found Reynolds number, voidage, and relative coordinates are the dominant factors, and a four-variable choice was demonstrated to output satisfactory performance for predicting local bubble diameter and velocity. The model was preliminarily validated by coupling with the EMMS drag into CFD codes, which showed that the accuracy of coarse-grid simulations can be significantly improved

Interaction network degree analysis.

<p>(A) Comparison of degree in PPI network of down_l versus down_h (left) and up_l versus up_h (right). (B) The correlation between degree and up/down-regulation number. The correlation curve is plotted by using the LOESS smoothing techniques and the shade indicates the confidence interval.</p

Pathway crosstalk among NAGenes-enriched pathways.

<p>Nodes represent pathways and edges represent crosstalk between pathways. Node size corresponds to the number of NAGenes found in the corresponding pathway. Node color corresponds to the P_BH-value of the corresponding pathway. Darker color indicates lower P_BH-value. Edge width corresponds to the score of the related pathways. Node shape indicates pathway categories, with ellipse for neurodevelopment, diamond for immune, triangle for metabolism, square for other pathways.</p

The comparison of baseline expression.

<p>(A) Comparison of earliest expression stage between down_l versus down_h (left) and up_l versus up_h (right). (B) The correlation between tissue expression specificity and up/down-regulation number. The correlation curve is plotted by using the LOESS smoothing techniques and the shade indicates the confidence interval.</p

Using time-varying quantile regression approaches to model the influence of prenatal and infant exposures on childhood growth

<p>For many applications, it is valuable to assess whether the effects of exposures over time vary by quantiles of the outcome. We have previously shown that quantile methods complement the traditional mean-based analyses, and are useful for studies of body size. Here, we extended previous work to time-varying quantile associations. Using data from over 18,000 children in the U.S. Collaborative Perinatal Project, we investigated the impact of maternal pre-pregnancy body mass index (BMI), maternal pregnancy weight gain, placental weight, and birth weight on childhood body size measured 4 times between 3 months and 7 years, using both parametric and non-parametric time-varying quantile regressions. Using our proposed model assessment tool, we found that non-parametric models fit the childhood growth data better than the parametric approaches. We also observed that quantile analysis resulted in difference inferences than the conditional mean models in three of the four constructs (maternal per-pregancy BMI, maternal weight gain, and placental weight). Overall, these results suggest the utility of applying time-varying quantile models for longitudinal outcome data. They also suggest that in the studies of body size, merely modelling the conditional mean may lead to incomplete summary of the data.</p

Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction

<div><p>Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction.</p></div

The overall view of the analysis.

<p>(A) The pipeline for the calculation of DEN of every gene from the CMAP dataset and the following computational analysis. (B) The distribution of down-regulation number (left) and up-regulation number (right) among the analyzed genes.</p

The evolutionary characteristic of genes with high DEN.

<p>(A) Comparison of homologous gene number between down_l versus down_h (left) and up_l versus up_h (right). ***, <i>P-Value</i> < 0.001 by Wilcoxon test. (B) Histogram comparing the fraction of genes in each phyletic age group.</p