Personalization in Skipforward, an Ontology-Based Distributed Annotation System

Abstract. Skipforward is a distributed annotation system allowing users to enter and browse statements about items and their features. Items can be things such as movies or books; item features are the genre of a movie or the storytelling pace of a book. Whenever multiple users annotate the same item with a statement about the same feature, these individual statements get aggregated by the system. For aggregation, individual user statements are weighted according to a competence metric based on the constrained Pearson correlation, adapted for Skipforward data: A user gets assigned high competence with regard to the feature in question if, for other items and the same feature type, he had a similar opinion to the current user. Since the competence metric is dependent on the user currently viewing the data, the user’s view of the data is completely personalized. In this paper, the personalization aspect as well as the item and expert recommender are presented.

Real-time qPCR validation of changes in expression of eight selected genes observed in microarray analysis.

<p>Bars represent the mean ± SD of fold change relative to growth medium control. At least 3 independent experiments were performed for each treatment. All genes were normalized to GAPDH. Asterisks placed vertically denote p values for each PIC treatment relative to HEC used as a reference. (***p<0.0005, **p<0.005, *p< 0.05; Student t-test)</p

Top network of Vk2 PIC/NIC discriminatory genes generated by Ingenuity Pathway Analysis.

<p>Red/pink color indicates upregulation of the genes (microarray data). Connections of NFkB complex with other genes is shown in blue color.</p

Transcription profile of the 20 discriminatory genes expression in Vk2 cells exposed to candidate microbicides and selected PICs and NICs.

<p>Columns represent treatments, rows represent genes. Gene expression levels are indicated by color: red is for upregulation and green is for downregulation. Expression data are averages from at least six experiments/microarrays for each treatment. Clustering based on 20 PIC/NIC discriminatory genes places C31G (known as causing inflammatory response) to the PIC category, while dextran sulfate (DS) and cellulose sulfate (CS)—into the NIC group.</p

Diagrams showing the number of significantly altered probesets indentified by microarray gene profiling of Vk2 cells exposed to PIC and NIC.

<p>Total number of the altered probeserts for each treatment/category is shown in brackets (a gene can be represented by more than one probeset).</p

Real-time qPCR validation of changes in expression of eight selected genes observed in the microarray analysis of microbicide candidates.

<p>Experimental details are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128557#pone.0128557.g005" target="_blank">Fig 5</a>. HEC and TNF-α are added as references.</p

PIC-DG expression following bacterial colonization of Vk2 cells as revealed by quantitative real time RT-PCR.

<p>P. bivia (right) induced strong upregulation of all seven PIC-DEGs, while L. gasseri (left) did not cause any changes. Results are presented as mean ±SD of three experiments.</p

Genes differenially expressed in VK2 cells treated with proinflammatory/immunomodulatory compounds.

<p>Genes differenially expressed in VK2 cells treated with proinflammatory/immunomodulatory compounds.</p

Functional categories of the PIC/NIC discriminatory genes^a.

<p>^<b>a</b>Classification is based on IPA functional analysis and published literature. P values are estimated by IPA</p><p>Functional categories of the PIC/NIC discriminatory genes<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128557#t004fn001" target="_blank">^a</a>.</p

Number of differentially expressed probesets in treatment groups compared to control (growth medium).

<p>Number of differentially expressed probesets in treatment groups compared to control (growth medium).</p