Dizeez - game review interface.

<p>Dizeez - game review interface.</p

Gene-disease associations provided four or more times in Dizeez and not found in Gene Wiki.

<p>Gene-disease associations provided four or more times in Dizeez and not found in Gene Wiki.</p

Gene-disease associations provided seven or more times in Dizeez.

<p>Gene-disease associations provided seven or more times in Dizeez.</p

Dizeez - main game interface.

<p>Dizeez - main game interface.</p

Number of Gene-Disease assertions vs. number of votes, for real- and random gameplay.

<p>The vertical axis represents the number of associations collected during game play (log scale). Red line: real gameplay. Grey bars: mean number of associations after 100 randomizations, with associated standard deviation. ‘7+’ indicates the sum of associations collected with a number of votes equal or greater than 7.</p

Concordance between Dizeez-mined associations and Disease and Gene Annotations database.

<p>The ‘concordance ratio’ on the vertical axis is the ratio between the associations supported by DGA and the total number of associations for a given number of votes. ‘7+’ indicates the sum of associations collected with a number of votes between 7 and 11.</p

Force11Putman_48_44.pdf

This is a poster generated to display our work in microbial data aggregation and integration at the Force2016 Conference in Portland, Oregon April 17-19th

wikidata: a central hub of linked open life science data

<p>Data in the life sciences are abundant, but dispersed over many different resources. However, for the onset of research these different resources need to be integrated. Although the Semantic Web has been proposed as a potential solution for rapid knowledge integration, most data remains in their different data silos, which expand continually, worsening the knowledge integration challenge.</p> <p>In the last decade, Wikipedia has been successful in becoming one of the most important sources of information on the web. Wikipedia thrives on the community for its curation. One of the partner projects currently is Wikidata, which is a public and free linked database using the same principles of community curation.</p> <p>Here we report on our effort to make Wikidata a central hub for linked open life science data. Doing so not only provides a linked data platform of said data, but also opens up the potential of the Wikidata community at large for curating and putting the different data sources under scrutiny. Our game plan is to (1) develop bots to publish knowledge from established data sources on genes, diseases and drugs on Wikidata, (2) harvest links between these entities and enrich the respective Wikidata entities with these relations, and (3) engage the community in curating the knowledge at hand by developing applications to disseminate the content to a wider audience. Here, we report the first milestones, being Wikidata entries on all human genes from Entrez Gene and the diseases from the Disease Ontology. Within days upon first publication of these entries, the curation power of Wikidata became visible by some valuable improvements made by the community. Our next goals are to add gene-disease, drug-disease and gene-drug relationships.</p

Ten Simple Rules for Cultivating Open Science and Collaborative R&D

<p>Ten Simple Rules for Cultivating Open Science and Collaborative R&D</p

The Implicitome: A Resource for Rationalizing Gene-Disease Associations

<div><p>High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [<a href="https://www.force11.org/group/fairgroup" target="_blank">https://www.force11.org/group/fairgroup</a>] using nanopublications. An online tool (<a href="http://knowledge.bio" target="_blank">http://knowledge.bio</a>) is available to explore established and potential gene-disease associations in the context of other biomedical relations.</p></div