A transversal approach to predict gene product networks from ontology-based similarity-1

<p><b>Copyright information:</b></p><p>Taken from "A transversal approach to predict gene product networks from ontology-based similarity"</p><p>http://www.biomedcentral.com/1471-2105/8/235</p><p>BMC Bioinformatics 2007;8():235-235.</p><p>Published online 2 Jul 2007</p><p>PMCID:PMC1940024.</p><p></p>ow curve represents the number of gene products that is weighted by the normalized number of terms. This curve reaches its maximum for the fifth level interval which corresponds then to the

A transversal approach to predict gene product networks from ontology-based similarity-2

<p><b>Copyright information:</b></p><p>Taken from "A transversal approach to predict gene product networks from ontology-based similarity"</p><p>http://www.biomedcentral.com/1471-2105/8/235</p><p>BMC Bioinformatics 2007;8():235-235.</p><p>Published online 2 Jul 2007</p><p>PMCID:PMC1940024.</p><p></p>urve) according to each threshold. The combination of the criteria of selection, i.e. high degree of similarity and high number of gene products per networks, leads us to choose a threshold of .65

A transversal approach to predict gene product networks from ontology-based similarity-3

<p><b>Copyright information:</b></p><p>Taken from "A transversal approach to predict gene product networks from ontology-based similarity"</p><p>http://www.biomedcentral.com/1471-2105/8/235</p><p>BMC Bioinformatics 2007;8():235-235.</p><p>Published online 2 Jul 2007</p><p>PMCID:PMC1940024.</p><p></p>rness, only the highest similarity links are represented. (b) Amine metabolism network. (c) Lipid metabolism and Catabolism. Up-regulated, Down-regulated and invariant genes are represented respectively as red hexagons, green boxes and yellow ellipses

A transversal approach to predict gene product networks from ontology-based similarity-5

<p><b>Copyright information:</b></p><p>Taken from "A transversal approach to predict gene product networks from ontology-based similarity"</p><p>http://www.biomedcentral.com/1471-2105/8/235</p><p>BMC Bioinformatics 2007;8():235-235.</p><p>Published online 2 Jul 2007</p><p>PMCID:PMC1940024.</p><p></p>se selected terms, term vectors are obtained for each gene products through the application of the idf weighting scheme, c) The comparison of these vectors in a Vector Space Model results in a matrix of similarity, d) Standard expression clustering based methods result in the attribution of each gene products to an expression cluster (up-regulated cluster, down-regulated cluster and invariant cluster i.e. constant expression cluster), e) Based on higher pairwise similarity, the matrix of similarity is displayed as a biological network where the gene expression clustering corresponds to the shape and color of each node. Up-regulated and Down-regulated genes are represented respectively as red hexagons and green boxes

A transversal approach to predict gene product networks from ontology-based similarity-0

<p><b>Copyright information:</b></p><p>Taken from "A transversal approach to predict gene product networks from ontology-based similarity"</p><p>http://www.biomedcentral.com/1471-2105/8/235</p><p>BMC Bioinformatics 2007;8():235-235.</p><p>Published online 2 Jul 2007</p><p>PMCID:PMC1940024.</p><p></p>se selected terms, term vectors are obtained for each gene products through the application of the idf weighting scheme, c) The comparison of these vectors in a Vector Space Model results in a matrix of similarity, d) Standard expression clustering based methods result in the attribution of each gene products to an expression cluster (up-regulated cluster, down-regulated cluster and invariant cluster i.e. constant expression cluster), e) Based on higher pairwise similarity, the matrix of similarity is displayed as a biological network where the gene expression clustering corresponds to the shape and color of each node. Up-regulated and Down-regulated genes are represented respectively as red hexagons and green boxes

Examples of the range of marker immunopositivity within normal adult brain and high-grade gliomas.

<p>Sections of paraffin-embedded specimens from six normal brain tissues and 96 HGG specimens, consisting of WHO grade III and IV glioma samples, were stained by immunohistochemistry with antibodies against EDN/RB, HJURP, p60/CAF-1 and PDLI4. Representative data are reported for each protein: a section of normal adult brain tissue, a section from a weakly positive tumour and a section from a strongly positive tumour.</p

Overall survival analyses of molecular markers.

<p>Kaplan–Meier estimates of overall survival are presented for all markers (EDN/RB, HJURP, p60/CAF-1 and PDLI4), after subdivision of the cohort of patients into two groups (low and high risk of death) on the basis of the cut-off points defined by analyses of time-dependent ROC curves. Panel A. For the EDN/RB protein, median overall survival for low-risk patients was 18.5 months (95% CI, 14.9-69.7), whereas that for high-risk patients was 14 months (95% CI, 10.4-18.3) (P=0.007). Panel B. For the HJURP protein, the difference in overall survival between low-risk and high-risk patients is significant (P=0.01 with 38.8 months [95% CI, 29.4-12.5] versus 14.9 months [95% CI, 12.5 to 17], respectively). Panel C. For the p60/CAF-1 protein, the difference in overall survival between high-risk and low-risk patients was also significant (p=0.004, 14 months [95% CI, 11.4-16.2] versus 23.5 months [95% CI, 16.8-55.8], respectively). Panel D. For the PDLI4 protein, the difference is also significant (P=0.02, 14.9 months [95% CI, 13-18.2] versus 19.6 months [95% CI, 16.7-Inf]).</p

Immunohistochemical analyses of marker expression in grade III and grade IV gliomas.

<p>A statistical analysis of the difference in the percentage of positive cells for each marker between grade III (32 cases) and grade IV specimens (64 cases) is presented. P-values were obtained by applying a Wilcoxon rank sum test to each comparison.</p

Number of genes regulated by disruption by mouse strain and organ studied

<p><b>Copyright information:</b></p><p>Taken from "Gene expression profiling of liver and duodenum in mouse strains with differing susceptibilities to iron loading: identification of transcriptional regulatory targets of Hfe and potential hemochromatosis modifiers"</p><p>http://genomebiology.com/2007/8/10/R221</p><p>Genome Biology 2007;8(10):R221-R221.</p><p>Published online 18 Oct 2007</p><p>PMCID:PMC2246295.</p><p></p> Genes regulated by disruption identified by statistical analysis of microarrays (SAM) were filtered to summarize the number of upregulated or downregulated genes in liver and duodenum. Genes were included if the mean S-score across three independent comparisons was ≥2 or ≤-2

Multivariate analyses of survival prognostic factors.

<p>NA = not available; N = not enough events to calculate upper 95% CI boundary; NS = not significant. For ordered categorical factors, the first value is the reference.</p><p>Multivariate analyses of survival prognostic factors.</p