ROC and precision-recall graphs for GO code predictions made by the system aggregated over the three ontologies

<p><b>Copyright information:</b></p><p>Taken from "Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S18-S18.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869010.</p><p></p> The solid line represents predictions made by the Informative Term/Naïve Bayes path in Figure 1. The dashed line represents predictions made by the Regular Expression/Naïve Bayes path in Figure 1

ROC and precision-recall graphs for GO code predictions made by the system aggregated over the three ontologies, when Informative Term models were learned without using any weakly labeled data

<p><b>Copyright information:</b></p><p>Taken from "Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S18-S18.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869010.</p><p></p> The solid line represents predictions made by the Informative Term/Naïve Bayes path in Figure 1. The dashed line represents predictions made by the Regular Expression/Naïve Bayes path in Figure 1

A high score in the test indicates that it is unlikely that the term is uncorrelated with the GO code

<p><b>Copyright information:</b></p><p>Taken from "Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S18-S18.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869010.</p><p></p

The input line is a sentence from an XML-formatted Journal of Biological Chemistry () document

<p><b>Copyright information:</b></p><p>Taken from "Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S18-S18.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869010.</p><p></p> The output line shows the result after standardization

Scatterplots for GO code predictions made by the various systems on the test set for Task 2

<p><b>Copyright information:</b></p><p>Taken from "Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S18-S18.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869010.</p><p></p>1 (left) and Task 2.2 (right). The graphs plot system precision against the number of true positive predictions (we do not plot recall because the total number of positive instances is unknown)

There is one Informative Term model for every GO code with sufficient training data

<p><b>Copyright information:</b></p><p>Taken from "Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S18-S18.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869010.</p><p></p> There are six Naïve Bayes models: one for each ontology and for each method of GO code prediction

A brief summary of reviewed methods.

<p>Icons arranged in the table represent individual methods. The columns represent the various experiment selection criteria, and the methods are divided vertically between de novo methods and methods that use prior knowledge. Visual elements in each icon indicate whether the method is deterministic (cog) or stochastic (die), whether it models continuous (circle) or discrete (diamond) variables, what is specified in a query for an experiment (G for genetic and E for environmental perturbations), and the dimensionality of the data used (dot array for multidimensional data and a ruler for one-dimensional data).</p

Functional Modules Identified by the “Localization Influence”

<div><p>(A) The AHR–GFP fusion protein translocates to nucleus in the presence of agonist βNF. Nucleus position in the cell was confirmed by DAPI staining (data not shown). Dimethyl sulfoxide (DMSO) is a vehicle control for βNF.</p> <p>(B) Classification of modifier deletion strains according to AHR–GFP phenotype (with βNF). Group I displays <i>wt</i> phenotype. Group II contains decreased level of receptor protein. Group III contains aggregated misfolded receptor. Group IV displays the AHR that is not capable of translocating to the nucleus.</p> <p>(C) Overlay of “localization influence” layer (shadowed, red boundary) and the “pharmacology clustering” layer (black boundary) on the AHR–PIN. Group I corresponds to modules C and D. Groups II, III, and IV overlap with modules of B, A, and E, respectively. Functional annotations determined by localization influence are indicated in red, and those derived from pharmacology clustering and domain influencing studies are indicated in black. Occasional outlier nodes are noted with their corresponding module designation. See the legend of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020065#pbio-0020065-g003" target="_blank">Figure 3</a> for the color scheme of the nodes and links.</p></div

Functional Modules Identified by Network Clustering

<div><p>(A) Network clustering of AHR–PIN. Protein nodes in the AHR–PIN (D_max = 2) were clustered by a hierarchical clustering algorithm. A tree-depth threshold was set to delimit cluster boundaries (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020065#pbio-0020065-Rives1" target="_blank">Rives and Galitski 2003</a>). Clusters with at least two M-nodes are shown. See text for details.</p> <p>(B) Overlay of the network clusters on the AHR–PIN. The ten network clusters correspond to ten local areas in the AHR–PIN. Each network cluster (local area) is labeled with its significant functional enrichment as calculated using the FunSpec program (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020065#pbio-0020065-Robinson1" target="_blank">Robinson et al. 2002</a>).</p> <p><b>Color scheme.</b> Nodes: modifier deletions that incurred down- and up-regulation of AHR signaling are marked in green and red, respectively. For intervening nodes, essential genes are marked in gray and nonessential genes in white. Links: physical interactions are labeled in black and genetic interactions in red. If both interactions are available for a given link, only the physical interaction is shown. This color scheme is also applied to Figures 4–7.</p></div

Regulatory Network of AHR Signaling

<div><p>(A) The summary map of AHR–PIN. Functional modules were determined by the overlapped annotations from three experimental layers (domain influence, pharmacology clustering, and localization influence) as well as from network clustering. For each functional module, the main “stacking pattern” of experimental layers is noted in italics. Modifiers initially left outside the single large cluster of the AHR–PIN were assigned to corresponding functional modules by sharing the similar stacking pattern where applicable. See the legend of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020065#pbio-0020065-g003" target="_blank">Figure 3</a> for the color scheme of the nodes and links.</p> <p>(B) An expanded model of AHR signaling. The AHR signaling pathway is regulated by at least five functional modules that are involved in the control of receptor folding, nuclear translocation, transcriptional activation, receptor level, and a PASB-related nuclear event. Within each functional module, modifers intially enclosed in the single large cluster of the AHR–PIN are highlighted in bold. Known human homologs of the modifiers are noted at the side with a smaller font (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020065#pbio-0020065-Costanzo1" target="_blank">Costanzo et al. 2001</a>) . ARNT is dimmed because modifiers were identified in this study from an “ARNT-free” chimeric AHR system. See text for details.</p></div