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FAQ

What is the Disease Ontology browser website?

The Disease Ontology browser is a web application that allows for exploration of the Disease Ontology. It allows for full text searching over the whole ontology as well as visualization of relations between nodes.

How often is the Disease Ontology browser updated?

The Disease Ontology browser is updated every night to match the latest Disease Ontology website.

What do the "Match Any" or "Match All" mean in the Advanced Search menu

This option specifies how to combine any advanced searches; "Match Any" is equivalent to a boolean OR search while "Match All" is equivalent to a boolean AND search

What technology is used for visualization on the Disease Ontology browser website

Several graphic libraries and software packages were evaluated for the handling of visualization of ontology relationships. The Arbor.js library (Arbor.js) was selected due to its strong cross-platform compatibility. Arbor.js is an actively developed javascript library that creates a force-directed graph utilizing the HTML5 canvas elements. Though HTML5 is a fairly new specification to the existing HTML standard, it is widely supported and the Abor.js library is compatible across all major browsers: Firefox, Chrome, Safari, Opera and Internet Explorer (version 9 and above) and many of the latest smart phone devices and tablets such as the Apple iPhone and iPad

Why does the visualization functionality not work in Interet Explorer

Internet Explorer does not support the HTML5 standard canvas element. The Disease Ontology browser makes use of the canvas element to visualize our relations between terms and thus will not work in Internet Explorer.

How do I download the Disease Ontology logical definition file (HumanDO_xp.obo)?

DO's logical definition file can be retrieved from the DO's Sourceforge site

Where can I submit a question or term request?

New terms, definitions, suggestions and questions regard the Disease Ontology can be submitted to the DO Term Tracker

How are data searched in the DO web site? What fields are searchable?

The web site search is based on concept mapping with a Lucene scoring index of matches based on word matches, in order, to name, synonym, definition (including definition xrefs), subset, DOID, alternative ID and xref. The Lucene scoring index weighs matches in order (highest to lowest): name, synonym, definition, subset, and ID). Consequently, matches for Disease Ontology terms where the name, synonym and definition all match the query term would rank higher (e.g. hepatitis A) than a Disease Ontology term where only a word in the name matched the query term (e.g. hepatitis).

How do I navigate around the DO web site?

Selecting a term in the Navigation Panel from the Disease Ontology tree or the Search results tab will load the selected terms metadata into the Metadata panel. Each search will create a new 'Search' results tab. Navigate between these tabs to view searches results and metadata. For more detailed instructions please visit the tutorial page.

Why does the browser back button not work.

The DO web site is presented with data presented in multiple tabs on a single web page. Selection, visualization or searching of data are all contained within this single page. Therefore, clicking on a browsers back button will not return you to the last piece of data viewed.

Does the DO browser have an API that can be accessed in a programmer-friendly way?

The DO browser does have an API that can be accessed via HTTP requests in a programmatic fashion. Currently only retrieving term metadata is supported but in the future we hope to expand the functionality. More information can be found on the tutorial page

What database backend does the Disease Ontology browser use?

The Disease Ontology browser uses the Neo4j technology to store all our ontology metadata. Neo4j falls under the umbrella of NoSQL databases (Wikipedia) being an embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs. Since Neo4j is a graph-based persistence engine, representing graph structures that include multiple relationships is very easy and data retrieval is very fast. Where a relational database might store ontology terms in one table with a one-to-many connection to another table containing relationships, a graph database stores terms as nodes that are connected to each other by edges (relationships).