Proteins in network proximity to HCV targets are highly enriched with lists of proteins proposed as regulators of host response to HCV and involved in HCC.

<p>The table lists the log₁₀ of the <i>p</i>-values that estimate the probability of obtaining, by chance (hypergeometric test), the observed overlap between the list of proteins from the literature (source and description, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113660#pone.0113660.s004" target="_blank">Tab. S2</a>) and the top ranked 1,500 proteins in network proximity to HCV on the basis of <i>s_i</i> or <i>p_i</i>, including or excluding HCV targets; NORM  =  normal, CIR  =  cirrhosis, DYS  =  dysplasia, eHCC  =  early HCC, aHCC  =  advanced HCC.</p><p>Proteins in network proximity to HCV targets are highly enriched with lists of proteins proposed as regulators of host response to HCV and involved in HCC.</p

Top ranked proteins in network proximity to HCV targets.

<p>The top ranked 1,500 proteins by network proximity score <i>s_i</i> (on the right of the dotted vertical line) or <i>p</i>-values (above the dotted horizontal line); red: HCV targets; black: non-HCV targets; point size is proportional to the number of interactions; labels indicate the top 10 of each ranking.</p

Most relevant relationships between proteins in network proximity to HCV and differentially expressed in preneoplastic and neoplastic liver samples.

<p><b>A</b>) CIR-NORM. <b>B</b>) HCC-CIR. <b>A–B</b>) Colors, from blue (lower values) to red (higher values): average fold change; squares: HCV targets; circles: non-HCV targets.</p

Random subnetworks in network proximity to HCV targets are more enriched in differentially expressed genes than random subnetworks.

<p><i>p</i>-values were calculated using two-sample Kolmogorov-Smirnov (KS) and two sample Wilcoxon-Mann-Whitney (WMW) tests between the enrichment values (<i>f</i>₂) for differential expression of 1,000 HRND and 1,000 RND subnetworks.</p><p>Random subnetworks in network proximity to HCV targets are more enriched in differentially expressed genes than random subnetworks.</p

Differential expression in network proximity to HCV proteins.

<p><b>A</b>) Gene expression variation (log₁₀(<i>f</i>₁)) and network proximity (log₁₀(<i>f</i>₂)) of optimal networks (Pareto fronts) identified for CIR-NORM and HCC-CIR comparisons. <b>B</b>) Estimated cumulative probability functions of gene expression variation (<i>f</i>₁) of 1,000 random networks (RND) and 1,000 HCV associated random networks (HRND) in CIR-NORM and HCC-CIR comparisons. <b>A–B</b>) the lower the value of <i>f_i</i> the higher the enrichment in the corresponding quantity.</p

Enrichment in HCV targets of differentially expressed genes in preneoplastic and neoplastic liver lesions.

<p><i>P</i>-values (<i>p</i>) were computed with Gene Set Enrichment Analysis (GSEA) and hypergeometric (hyper) test.</p><p>Enrichment in HCV targets of differentially expressed genes in preneoplastic and neoplastic liver lesions.</p

Most relevant relationships between proteins in network proximity to HCV proteins.

<p>MSTs among the (<b>A</b>) 100 proteins with the highest network proximity score (<i>s_i</i>) and (<b>B</b>) 100 proteins with the highest <i>s_i</i> excluding HCV targets, using edge weights (<i>w_ij</i>) inversely proportional to the product of proteins network proximity scores: <i>w_ij</i> = 1 - <i>s_is_j</i>. <b>A–B</b>) The darker the color, the higher the network proximity score; squares: HCV targets; circles: non-HCV targets; vertex size is proportional to the number of interactions in the host interactome.</p

Writing electronic nursing care plans. An approach to facilitate navigating the standardized Nursing vocabularies NANDA and NIC

The introduction of the standardized nursing languages NANDA and NIC to write electronic care plans is hard for nurses, as reported by several projects. Both the need to relate to a new software tool and to the totally unknown domains of the standardized languages contributes to this. With an existing tool for care plan writing as basis, we have used the unified process to develop an improved tool, in order to make the introduction smother. Care has been taken to make the interface and workflow as intuitive as possible, allowing novice computer users to use the tool. An automatic presentation of the information in the languages is implemented, to further meet the needs of unskilled users. To ease navigation in the classifications of the standardized languages, a three level harmonized classification for the two languages was chosen. The two uppermost levels of the classification are presented in one screen, resulting in an efficient browsing for NANDA diagnoses and NIC interventions. To make it possible for users unacquainted with the language to find NANDA diagnoses, a search facility was developed. An information retrieval method was implemented, making it possible to search for diagnoses on the basis of signs and symptoms. The query interface was deliberately made as simple as possible; one or more keywords are entered, and a list of diagnoses is returned ranked according to relevance to the keywords. The search facility also helps in the process of exploring and learning the classification hierarchy. Usability tests shows improvement both in ease of use and support for exploring the standardized languages for the new system compared with the original

A data integration approach for cell cycle analysis oriented to model simulation in systems biology-2

<p><b>Copyright information:</b></p><p>Taken from "A data integration approach for cell cycle analysis oriented to model simulation in systems biology"</p><p>http://www.biomedcentral.com/1752-0509/1/35</p><p>BMC Systems Biology 2007;1():35-35.</p><p>Published online 1 Aug 2007</p><p>PMCID:PMC1995223.</p><p></p>ycle model from Swat et al 2004. Using the web interface it is possible to select which model component will be shown: in this case the graph plots the time course for the CyclinE-Cdk2 complex concentration in the inactive state (CycEi), the CyclinE-Cdk2 complex concentration in the active state (CycEa) versus the time course for pRB and E2F1 concentrations