AUPR and AUROC values for mouse datasets using BWERF and GENIE3.

<p>AUPR and AUROC values for mouse datasets using BWERF and GENIE3.</p

Flowchart illustrating BWERF algorithm for constructing multilayered hierarchical gene regulatory network using expression data of pathway genes and regulatory genes.

<p>A. Input for BWERF included a pathway gene expression matrix and a TFs expression matrix. B. For each pathway gene, recursively constructing of random forest model with backward elimination. C. Aggregation of the importance values of a TF to all pathway genes to produce a unified the importance value of this TF to the pathway. D. The Expectation-maximization (EM) algorithm was implemented to fit a Gaussian mixture model to the importance values. E. The most important TFs were identified and used as a layer. F. By using the new TF layer as bottom layer, we repeated all above procedure to obtain the next layer until the designated number of layer was achieved or the program was terminated due to the lack of significant TFs as input for upper layers.</p

Construction of ML-hGRN for lignocellulosic pathway with a compendium microarray data set (128 chips) from <i>Arabidopsis thaliana</i> roots under salt stress condition.

<p>A. The four-layered hGRN constructed with the BWERF algorithm. B. The four-layered hGRN built from GENIE3. The input files for BWERF and GENIE3 included the expression profiles of 1602 transcription factors and 22 lignocellulosic pathway genes (green nodes at bottom layers). The nodes with red color highlighted in both networks are known regulatory TFs regulating lignocellulosic pathway in existing knowledgebase. The data, edge list as well as gene IDs represented by each symbol can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171532#pone.0171532.s002" target="_blank">S2 File</a>.</p

The precision-recall (PR) and receiver operating characteristic (ROC) curves of BWERF and GENIE3 for three mouse microarray data sets.

<p>The 24 stem cell pluripotency maintenance genes regulated by the 35 transcription factors (TFs) were obtained from mouse ChIP-seq data. A microarray data set of these pathway genes and TFs yielded from a time course in which the pluripotent cells were subjected to undirected differentiation were downloaded from ESCAPE web portal (<a href="http://www.maayanlab.net/ESCAPE/" target="_blank">http://www.maayanlab.net/ESCAPE/</a>). The three data sets were generated by adding the profiles of 100, 200, and 300 noise variables to the profiles of 35 TF genes for <i>in silico</i> experimental validation.</p

Comparison of the ordered important values of top 15 variables generated by GENIE3 (left) and BWERF (right) on a “toys data set”.

<p>Red: true regulators, blue: noise variables. The boxplots were based on 30 runs of GENIE3 and BWERF.</p

Genome enrichment analysis for genomic correlates.

<p>Genomic correlates are likely disrupted in Ama-KTT/M/2 versus Canton S (red) and Ama-KTT/M/2 on α-amanitin versus Ama-KTT on non-toxic food (blue). Colored lines above the gray line indicate significant enrichment of a genomic correlate. Of the five genomic correlates rising above the cutoff value, two genomic correlates are similar to those found in previous linkage studies on the Ama-KTT stock.</p

The Mechanisms Underlying α-Amanitin Resistance in <i>Drosophila melanogaster</i>: A Microarray Analysis

<div><p>The rapid evolution of toxin resistance in animals has important consequences for the ecology of species and our economy. Pesticide resistance in insects has been a subject of intensive study; however, very little is known about how <i>Drosophila</i> species became resistant to natural toxins with ecological relevance, such as α-amanitin that is produced in deadly poisonous mushrooms. Here we performed a microarray study to elucidate the genes, chromosomal loci, molecular functions, biological processes, and cellular components that contribute to the α-amanitin resistance phenotype in <i>Drosophila melanogaster</i>. We suggest that toxin entry blockage through the cuticle, phase I and II detoxification, sequestration in lipid particles, and proteolytic cleavage of α-amanitin contribute in concert to this quantitative trait. We speculate that the resistance to mushroom toxins in <i>D. melanogaster</i> and perhaps in mycophagous <i>Drosophila</i> species has evolved as cross-resistance to pesticides, other xenobiotic substances, or environmental stress factors.</p></div

Effects of different salt treatments on net ion rates of <i>Elaeagnus angustifolia</i> seedlings.

<p>Net Na⁺ rates (<i>J</i>^Na, A) and net Cl^- rates (<i>J</i>^Cl, B) were determined in stem, leaf, shoot and whole plant of <i>E</i>. <i>angustifolia</i> seedlings hydroponically treated with 0, 100 and 200 mM NaCl for 30 days. Each vertical bar represents standard error (SE) for the mean (M) of three replications (n = 3). Different letters above bars denote significant levels between the different salt concentrations in a given plant part at 0.05 according to Duncan’s multiple-range test.</p

Effects of different salt treatments on dry weight accumulation and allocation of <i>Elaeagnus angustifolia</i> seedlings.

<p>Effects of different salt treatments on dry weight accumulation and allocation of <i>Elaeagnus angustifolia</i> seedlings.</p

Ama-KTT/M/2 is not less resistant to α-amanitin than Ama-KTT.

<p>Ten first-instar larvae were placed on each α-amanitin concentration. The dose response curve shows the percentage of hatching flies. Error bars indicate the s.e.m. of three replicates.</p