ReVeaLD: A user-driven domain-specific interactive search platform for biomedical research

Abstract

Bioinformatics research relies heavily on the ability to discover and correlate data from various sources. The specialization of life sciences over the past decade, coupled with an increasing number of biomedical datasets available through standardized interfaces, has created opportunities towards new methods in biomedical discovery. Despite the popularity of semantic web technologies in tackling the integrative bioinformatics challenge, there are many obstacles towards its usage by non-technical research audiences. In particular, the ability to fully exploit integrated information needs using improved interactive methods intuitive to the biomedical experts. In this report we present ReVeaLD (a Real-time Visual Explorer and Aggregator of Linked Data), a user-centered visual analytics platform devised to increase intuitive interaction with data from distributed sources. ReVeaLD facilitates query formulation using a domain-specific language (DSL) identified by biomedical experts and mapped to a self-updated catalogue of elements from external sources. ReVeaLD was implemented in a cancer research setting; queries included retrieving data from in silico experiments, protein modeling and gene expression. ReVeaLD was developed using Scalable Vector Graphics and JavaScript and a demo with explanatory video is available at http://www.srvgal78.deri.ie:8080/explorer. A set of user-defined graphic rules controls the display of information through media-rich user interfaces. Evaluation of ReVeaLD was carried out as a game: biomedical researchers were asked to assemble a set of 5 challenge questions and time and interactions with the platform were recorded. Preliminary results indicate that complex queries could be formulated under less than two minutes by unskilled researchers. The results also indicate that supporting the identification of the elements of a DSL significantly increased intuitiveness of the platform and usability of semantic web technologies by domain users.

A Roadmap for Navigating the Life Sciences Linked Open Data Cloud

“…The user can also select relevant filters (numerical and textual) through the 'Topic-based' builder. Our implementation exposes a REST API and provides a visual query system, named ReVeaLD [12], for intuitive query formulation and domain-specific uses [10]. The catalogued subclasses of few Qe and as a result the total number of distinct instances retrieved per Qe while querying using DSQE is shown in Fig.…”

“…3). The linked concepts and literals are displayed as nodes arranged in a force-directed concept map representation, as previously introduced in [12]. The Qe used for linking are displayed as Light Brown-colored nodes, whereas catalogued concepts (Blue-colored nodes) are linked to these Qe using the voidext:subClassOf.…”

Linked Biomedical Dataspace: Lessons Learned Integrating Data for Drug Discovery

“…The KPC also provides a Visual Query System -ReVeaLD 13 (Real-time Visual Explorer and Aggregator of Linked Data) for facilitating non-technical biomedical users to intuitively formulate advanced SPARQL queries by interacting with a visual concept map representation of CanCO [18]. Results are aggregated from the LBDS and presented in a data browser with 'Smart Icons', which render domain-specific visualizations using a set of Qe-based Graphic Rules, and refer to additional information available on portals like ChemSpider [25] and PubChem [21].…”

“…SPARQL endpoints under the Bio2RDF Release 2 were integrated later in May 2013 due to better uptime. To aid the non-technical users we created a screencast 17 outlining the different steps (Workflow II, III), which was made available on the project website and has been downloaded by ∼175 users 18 by October 2013. Google Analytics tracking showed that ReVeaLD was accessed by 387 distinct users from 29 countries, up to February 2014 (Fig.…”

“…Our main motivation was to enable cancer researchers to retrieve information pertaining to their research questions. Previously, the domain experts have extensively evaluated the accuracy of our integration and the usability of our platform for information discovery [42,15,18]. During the development of the components we learnt important lessons by tackling the complex challenges associated with the complexity of biomedical data integration and discovery, and believe that our gained insights would be useful for LD practitioners.…”