Slopes of the dose-response curves in Figure 2.

a<p>The slopes are in terms of changes in the predicted z values per change in tabulated dose. ^b p-value for hypothesis testing the alternative that the estimated slopes are positive.</p

Dose-response predictions using the pathway-based prediction models for data of the chemicals tabulated in Table 2.

<p>Dose response predictions for five chemicals (a. dcbz, b. mecl, c. npth, d. pgbe and e. tcpn) treated at four different doses in mice.</p

Receiver-operator characteristics (ROC) curves of carcinogenicity predictions using the pathway-based prediction models across three species. (a. Mice, b. Rats and c. Humans)

<p>The legend in the sub-plots provides the area-under-the-curve (AUC) for the corresponding ROC curve. The curves for mice, rats and humans are based on datasets corresponding to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063308#pone-0063308-t001" target="_blank">Tables 1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063308#pone.0063308.s003" target="_blank">Table S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063308#pone-0063308-t003" target="_blank">Table 3</a> respectively.</p

Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species

<div><p>Background</p><p>Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases.</p><p>Objectives</p><p>To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions.</p><p>Methods</p><p>Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans.</p><p>Results</p><p>Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions.</p><p>Conclusions</p><p>Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.</p></div

Treatment groups and abbreviations used in the 90 day exposure to the 26 chemicals and corresponding vehicle controls used in this study.

a<p>I  =  inhalation; F  =  feed; GC  =  gavage, corn oil (5 ml/kg); GW  =  gavage, deionized water (5 ml/kg).</p>b<p>The results for liver tumors were based on a <i>p = 0.01</i> threshold for combined increase in adenomas or carcinomas.</p>c<p>The results for liver tumors in this study were considered equivocal or borderline significant. Combined increase in hepatocellular adenomas or carcinomas resulted in <i>p = 0.075</i> and <i>p = 0.084</i> for benzene and coumarin respectively.</p>d<p>Due to signs of toxicity, the 16,000 ppm dose was reduced to 0 ppm on day 9 for a period of 2 days. The dose was raised to 8,000 ppm for a period of 9 days and returned to 16,000 ppm for the remainder of the study. The time weighted average dose was 14,800 ppm.</p>e<p>The initial dose of 3,000 ppm was reduced to 2,000 ppm in week 2 of the study due to taste aversion and weight loss. The 2,000 ppm dose is the same as the low dose in the original bioassay.</p>f<p>Chemical not evaluated by the NTP. Bioassay performed by Alexander <i>et</i><i>al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063308#pone.0063308-Becker1" target="_blank">[20]</a>.</p

Dose response treatment groups and abbreviations used in the 90 day exposure with the results from the NTP rodent cancer bioassay.

<p>The vehicle controls were the same as given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063308#pone-0063308-t001" target="_blank">Table 1</a>.</p>a<p>I  =  inhalation; GC  =  gavage, corn oil (5 ml/kg).</p

Top 15 pathways of the fitted prediction model.

<p>Top 15 pathways of the fitted prediction model.</p

False positives and false negatives predictions of predictors across the three species at appropriately chosen points on the receiver-operator curves in Figure 1.

<p>False positives and false negatives predictions of predictors across the three species at appropriately chosen points on the receiver-operator curves in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063308#pone-0063308-g001" target="_blank">Figure 1</a>.</p

Human data sets associated with risk for liver cancer that were used for carcinogenicity predictions.

a<p>The first 13 data sets with IDs beginning GDS- or GSE- represent gene expression data obtained from GEO database (<a href="http://www.ncbi.nlm.nih.gov/geo/" target="_blank">http://www.ncbi.nlm.nih.gov/geo/</a>, accessed June 2009). The gene polymorphism data associated with various human diseases are obtained from the GAD database (<a href="http://geneticassociationdb.nih.gov/" target="_blank">http://geneticassociationdb.nih.gov/</a>, accessed June 2009).</p

A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data-0

<p><b>Copyright information:</b></p><p>Taken from "A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/228</p><p>BMC Bioinformatics 2007;8():228-228.</p><p>Published online 29 Jun 2007</p><p>PMCID:PMC1940027.</p><p></p>er degree of similarity between the different gene-expression patterns in the system, the network in (b), "Medium" in a medium degree of similarity, while the network in (c), "High" results in a relatively high degree of similarity. The units of time are arbitrary but are consistent with the units of the parameters of the system