Wikidata and biomedical information.pdf

Wikidata and biomedical information presentation for Wikimania 2017, in Montreal, Canada

GSC and MIxS - Standards for genomic, metagenomic and marker gene sequence data

<p>An introduction to GSC (Genomic Standards Consortium) and MIxS (Minimal Information for any=x sequence) from the ICBO 2015 conference.</p

Wikidata: A platform for data integration and dissemination for the life sciences and beyond

An introduction into how Wikidata can be used as a semantic platform for the life sciences and beyond

Making every human gene accessible and linkable

<p>What if every human had access to the sum total of all biomedical knowledge? Not only through articles in popular science periodicals or sensational tv documentaries, but real access that goes both ways, where the general public can engage and influence their biomedical scientists. Biomedical research is typically reported through articles in scientific journals (the collection of which increases by more than 1 million per year). Some information is structured and shared through literally thousands of different, partially overlapping databases. Both of these information resources are almost completely opaque to the general public because of paywalls and the challenges of querying multiple non­interoperable databases. Even professional scientists with both institutionally funded journal and database access face major challenges in assimilating the knowledge needed to generate effective new hypotheses. It is not uncommon for a scientist to spend a large part of their precious time on technicalities of data access and integration, time that would be better spent on finding a cure for cancer, for example.</p> <p>From its inception, Wikipedia has taken an entirely different approach to distributing knowledge. Topic­focused, encyclopaedic articles naturally lend themselves to interlinking and to evolution over time. Community ownership ensures broad access and, in the great majority of cases, results in high quality. Since 2005, Wikipedians have made a concerted effort to organize and improve the content of biomedically relevant articles via, for example, WikiProjectMolecular and Cellular Biology (https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Molecular_and_Cellular_Biology)and WikiProject Medicine. Recognizing the value of Wikipedia as a repository and foundry of this kind of knowledge, the NIH funded the Gene Wiki (https://en.wikipedia.org/wiki/Gene_Wiki)project in 2010 to help continue to stimulate growth and improve content focused on human genes.</p> <p>Now in its second iteration, the Gene Wiki project is coordinating with the recently released WikiData platform as it continues to advance its goals of democratizing access to biomedical knowledge. Wikidata is the centralized linked knowledge base for Wikimedia projects, such as all different language Wikipedias. Structured elements of Wikipedia articles such as tables can now be built dynamically from knowledge captured in Wikidata. As with Wikipedia articles, any Wikidata entry can be edited by anyone (both humans and computer programs). In the other direction, Wikidata provides interfaces, including a SPARQL endpoint, for external applications to query.</p> <p>As Wikipedia provides open access to an evolving collection of human readable articles, Wikidata provides access to an evolving trove of structured data. Together, these public resources provide the means for research communities to efficiently share their insights with each other as well as the public at large. The knowledge is there to use, outside the scope of any professional limitation. Not only is this a great way of disseminating knowledge, it also opens up scientific knowledge for public scrutiny. Anyone has access to the data, to references about where it came from, and to the discussion pages behind each Wikidata item where they can engage in discussing the quality of the knowledge added. Community input is broadcasted back to the original data owners, which has already lead to improvements in the source data. We are working to promote this two­way traffic such that it leads to higher quality scientific data and thus improves our collective understanding of ourselves and the world around us.</p

Open Biomedial Knowledge: Wikipedia, Wikidata and Beyond

<p>The Gene Wiki project emerged in 2008 to provide a way to centralize information about human genes in a human-readable fashion in the form of Wikipedia articles. Now, in the second phase of this initiative, this project is expanding its scope to improve the Wikipedia representation of information about genes, diseases, drugs and their inter-relations to both foster education and stimulate scientific progress.</p> <p>So far we have written bots that have generated Wikidata items for: all human genes, all mouse genes, all diseases represented in the Human Disease Ontology, and all FDA-approved drugs. We are currently working to expand our Wikidata representations of information about these items and are in the early phases of including this new content in the corresponding Wikipedia article infoboxes. We are actively seeking collaborators from the Wikipedia and Wikidata communities to help with all aspects of this initiative.</p

Linking Wikidata to the Semantic Web

Poster described at: http://ceur-ws.org/Vol-1795/paper46.pdf<div><br></div><div> <div> <div> <div> <p>Wikidata is the linked database of Wikipedia and its sister projects from the Wikimedia foundation. Wikidata can be queried by SPARQL queries. Either through the WikiData Query Service (WDQS: http://query.wikidata.org) or its SPARQL endpoint at: https://query.wikidata.org/bigdata/namespace/wdq/sparql. </p><p>Both the WDQS and the SPARQL endpoint do not allow submitting federated SPARQL queries. In our efforts to make Wikidata the central hub of linked data in the life science, being able to submit federated queries can be an asset. Although federation is not supported from Wikidata, the SPARQL endpoint is accessible for other SPARQL endpoint that do support federation. We compare four federated SPARQL query patterns to query Wikidata in combination with other semantic web formats. <br></p> </div> </div> </div></div

10 steps to integrate CIViCdb with other public data in Wikidata

<div>Precision medicine has shifted our understanding of the etiology and treatment of cancer from a focus on anatomical to molecular features. The genetic fingerprint of a patient can be deterministic in both the onset and treatment of the disease. However the etiological network of a specific disease consists of very diverse factors from genetic to environmental. With such diverse knowledge comes a diverse data infrastructure. Data is scattered across data silos and different data formats/structures. This poses a serious bottleneck when interpreting data in a clinical and/or research setting. </div><div>CIViC (http://www.civicdb.org) is an open-access, community-based, highly-curated cancer variant database. It is a platform where data on cancer genomic alterations from different data sources are curated and interpreted for clinical application. These interpretations with their evidence are captured and stored as structured data in the public domain. In order to reach an even broader audience an effort was made to include CIViC's data into Wikidata. Wikidata contains and feeds structured data into Wikipedia and to other Wikimedia projects. It has all the traits of Wikipedia (open-access, editable, community-driven) and is accessible to both humans and machines. Although Wikidata has a Wikipedia narrative, its application is not limited to it. The open APIs allow broader application. </div><div>Adding public domain datasets to Wikidata benefits audiences in both directions. In this case, Wikidata gains additional content from a highly-curated resource, while CIViC gains exposure to a wider audience, the ability to link to other data types and domains (e.g. drugs) and the benefits of Wikidata’s being a hub on the Semantic Web, allowing complex queries to be performed. We report the process involved in linking CIViC to Wikidata. This led to eight new Wikidata relations and a model to capture provenance. The resulting statements are built upon common standards coming from ontologies and other resources. The success of the data integration is proof that different data models can work together without any loss of information and we invite other resources to follow.</div